1
The use of financial contracting arrangements to constrain rent-seeking
behaviour of managers of subsidiary firms: Evidence from UK and US
multinational subsidiary firms
Abstract: We propose and test two hypotheses on how multinational (MNC) parents can use
different financial contracting arrangements when financing their subsidiaries in order to
constrain the rent-seeking behaviour of subsidiary management. Furthermore, we also
examine how the MNC parent can effectively enhance the monitoring of their subsidiaries by
delegating this function to external financial institutions. One hypothesis analyses the use of
short-term debt. The second hypothesis investigates the use of short-term external debt.
Moreover, our analysis is enriched by investigating these two hypotheses in two different
settings, namely by comparing between UK domestic and UK subsidiary firms and by
comparing UK and US subsidiary firms. This enables us to measure the subsidiary effect and
the location/distance effect. Using an unbalanced panel data of 11762 and 10096 firm-year
observations we find that UK subsidiaries have more short-term debt and more short-term
external debt compared to equivalent UK domestic firms and that US subsidiaries have more
short-term debt and less short-term external debt compared to equivalent UK subsidiaries.
Our results are both statistically and economically significant. We find these results in spite of
our domestic firms being larger, being more profitable, having higher growth opportunities,
and having higher tangibility, all factors which should translate in higher amounts of debt.
Our results are robust, even after controlling for all the variables that have been found to
affect leverage in the literature and when we use a matched sample approach to test our
hypotheses. Furthermore, we also consider and rule out other possible explanations for our
results.
Keywords: Subsidiary leverage; Parental control; Financial contracting; Parent-subsidiary
relationships; Multinational corporations
JEL Classification Codes: F23; G32
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The use of financial contracting arrangements to constrain rent-seeking behaviour of
managers of subsidiary firms: Evidence from UK and US multinational subsidiary firms
1. INTRODUCTION
(Agmon, 2006) asserted that finance had largely been ignored in international business
research and suggested that integrating finance into international business research would
greatly benefit both disciplines. More recently, following a similar argument, (Bowe et al.,
2010) suggested a few areas which could offer promising grounds for research. In particular,
they mentioned that it could be interesting to look at “how financing constraints can impact
upon the organisations ability to allocate and enforce control rights through appropriate
governance mechanisms” p. 435 and “the role of external financial institutions such as banks
and auditing firms, as delegated monitors and enforcers of control rights” p.436.
In this paper, we try to respond to that call by investigating the way that the multinational
(MNC) parent uses different financial contracting arrangements to finance their subsidiary
firms in order to constrain their rent-seeking behaviour. Furthermore, we also examine how
the MNC parent can effectively enhance the subsidiary monitoring by delegating this function
to external financial institutions. We develop a series of testable hypotheses based on the
theoretical and empirical literature in finance and international business and test these with
data for the period 2001 to 2010 on US MNCs’ UK subsidiary firms. This paper is largely
based on an unpublished manuscript by (Bowe and Yamin, 2003) whereby the authors had
developed the hypotheses and their rationales. This paper extends their work: (i) by refining
those hypotheses and updating them by integrating recent developments from the finance and
international business literature; and (ii) by empirically testing these hypotheses using UK
data.
To the best of our knowledge, this is the first time that someone is attempting to test how
financial contracting arrangements are used by parent firms in order to constrain the rentseeking of subsidiary managers. We contribute to the extant literature in several ways: First,
we contribute to the subsidiary control literature by proposing a new control mechanism that
MNC parents can use to effectively control and constrain managers of MNC subsidiaries;
second, we contribute to the subsidiary leverage literature by providing a new explanation for
differences in leverage between (i) stand alone and domestic subsidiary firms and (ii) between
UK domestic and US subsidiary firms. Third, while most of the literature investigates
differences in the total amount of leverage, we also contribute to the literature by
investigating differences in short-term and short-term external debt. To a lesser extent, our
paper is also one of the few that uses UK data and that uses data on MNCs subsidiaries rather
than MNCs parent data.
The rest of the paper is organised as follows: Section 2 provides a literature review of studies
related to MNCs subsidiary financing and MNCs leverage. Section 3 provides the theoretical
and empirical basis for suggesting an alternative subsidiary control mechanism. This section
also justifies and presents our testable hypotheses. Section 4 outlines our research design and
defines our variables, data sources, and sample selection method. Section 5 presents our
results while section 6 discusses robustness checks followed by our concluding remarks in
section 7.
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2. LITERATURE REVIEW
The early studies investigated the differences in financing between domestic firms and
MNCs. These studies were performed mainly through surveys with MNCs executives and,
amongst others, documented the additional complexities involved in financing decisions in
the multinational context and the factors which influence such decision-making. (Stobaugh,
1970; Robbins and Stobaugh, 1972; Errunza, 1979).
Subsequent studies investigated the empirical difference between the level of leverage
between purely domestic firms and MNCs by testing two arguments with opposite predictions
on this difference. On the one hand, given that MNCs operate in various different countries,
their activities are more diversified which contributes to lower their overall risk. As a result,
MNCs were expected to be able to borrow higher amounts of debt. On the other hand, given
that MNCs operate in various countries, it makes such entities more difficult to monitor thus
MNCs will have higher agency costs of debt. As a result, MNCs were expected to have lower
amounts of debt. However, the evidence of these studies has been mixed with some scholars
finding either lower amounts or higher amounts of debt for MNCs compared to domestic
firms (Fatemi, 1988; Lee and Kwok, 1988; Doukas and Pantzalis, 2003).
One of the reasons why the results of the previous studies comparing MNCs and domestic
firms’ leverage got mixed results is that they could have ignored other factors which influence
leverage in the MNC context. The subsequent literature has focused on investigating the
influence of such factors on MNCs leverage.
(Desai et al., 2004; Aulakh and Mudambi, 2005; Aggarwal and Kyaw, 2008; Dewaelheyns
and Van Hulle, 2010) document that MNCs use internal capital markets opportunistically and
to overcome shortcomings in either the home country or host country. (Sekely and Collins,
1988; Chui et al., 2002; Aggarwal and Goodell, 2010) found evidence that cultural factors
influence the capital structure of MNCs. (Stonehill and Stitzel 1969; Hooper, 2004;
Kesternich and Schnitzer, 2010) found that the level of political risk affects MNCs affiliates
debt levels and ownership share levels. With regards to exchange rates, (Errunza, 1979)
argued that they are an important factor that the MNC parent considers prior to deciding on
the MNC affiliate financing, especially if the MNC parent expects a devaluation between its
home currency and the host country’s currency. (Bowe and Dean, 2001) also document that
MNCs tend to try to hedge revenue and costs in a common currency to limit their exposure to
exchange rate risk. (Huizinga et al., 2008; Arena and Roper, 2010) document that differences
in taxation rates between the home and host country explain differences in capital structure
between MNCs and domestic firms. Furthermore, a large number of studies in this area use a
multiple country setting comparing leverage in several countries at the same time. In this
context, these studies of MNCs leverage found that differences in leverage could be explained
by differences in legal and institutional factors (La Porta et al., 1997; La Porta et al., 1998;
Bianco et al. 2005), creditor rights (Akbel and Schnitzer, 2011), securities law (Mishra and
Tannous, 2010); and bankruptcy codes (Acharya et al., 2011).
3. WHY IS FINANCING DIFFERENT IN THE MNC CONTEXT COMPARED
TO A DOMESTIC FIRM ?
When firms have to select a source of finance, they can choose between using internal sources
in the form of retained earnings or external sources in the form of bank debt, or issuing bonds,
hybrid securities or equity in the capital markets. Firms can decide to use one of these sources
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of finance a combination of any of these. In the case of a firm operating in one single country,
usually, all these sources of finance would be available within that same country.
Furthermore, in contrast to stand-alone firms, firms which belong to a group of companies
will have access to an additional source in the form of internal capital markets, i.e. funds from
other entities from the group. However, the choice of sources of finance gets even larger when
we deal with MNC firms operating in several countries. Indeed, they will be able to source
any of the funds already mentioned within each one of the countries in which they are
operating. Moreover, (Shapiro, 1975) argued that the financing choice might be the result of
cost-benefit analysis between using different sources of financing and other factors, such as
different currencies, taxation, institutional, and legal regimes.
3.1 The nature of the MNC organisation
(Birkinshaw and Hood, 1998) showed that the MNC is a federative organisation whereby a
central legally sanctioned authority does not represent how power is exercised within the
MNCs. Indeed, (Dorrenbacher and Gammelgaard, 2011) show that one must make a
distinction between formal and real authority, with the latter being the one that matters. In the
case of MNC subsidiary firms, (Andersson et al., 2002) documented that, over time, these
become embedded in host countries through the development of local networks. As a result,
subsidiary firms develop knowledge and gain resources that are valuable to the MNC network
as a whole. The latter enables the subsidiary firm to acquire “positive” power, i.e. a relative
degree of independence from the MNC parent to pursue its own initiatives, and, at the same
time, the MNC subsidiary also acquires “negative” power, i.e. the ability of not doing what
they are told to by the MNC parent. The power of the MNC subsidiary originates from the
fact that it has access to resources and knowledge which are not only exclusive to the MNC
subsidiary but are also opaque to the MNC parent. Furthermore, the MNC subsidiary
management might be tempted to use this power to pursue its own goals even if they are not
congruent to the objectives of the MNC as a whole (Mudambi and Navarra, 2004) (Ciabuschi
et al., 2011), (Chen et al., 2012). As a result, it is clear that the MNC parent must somehow
control its subsidiaries but has to be very careful in selecting the control mechanism. Indeed,
the MNC parent has to ensure that, on the one hand, their subsidiaries are given enough
freedom to pursue initiatives which will benefit the MNC organisation as a whole but, on the
other hand, ensure that the subsidiary is constrained so that the power that it acquires from its
initiatives is not used to maximise its benefits at the expense of the MNC organisation as a
whole.
3.2 Mechanisms proposed to control MNC subsidiaries
As a result of evidence that the MNC parent must control its subsidiaries, scholars have
explored several mechanisms that could be used to control the behaviour of the management
of MNC subsidiary firms. (Roth and O’Donnell, 1996) argued that parent-subsidiaries
relationships can be treated as principal-agent relationships and suggest making use of
compensation as a device to align the interests of subsidiary management with those of the
parent. A similar approach was suggested by (Costello and Costello, 2009). (Foss et al., 2006;
Thite et al., 2012) suggested using various HR policies, such as appointing the management
of the subsidiary from with the MNC organisation to ensure that goal congruence is achieved.
(Tsai and Ghoshal, 1998) argued that it is important to create social or relational capital as it
will enhance overall organisation solidarity. (Yamin and Forsgren, 2006; Anderson et al.
2007) argued that the MNC parent should get closer or even penetrate the MNC subsidiary
network through regionally focused MNCs. More recently, (Yamin and Sinkovics, 2007) have
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proposed using ICT systems to control MNC subsidiaries. However, to the best of our
knowledge, using financial contractual arrangements as a control mechanism has never been
suggested in the literature.
3.3 An alternative monitoring device that constrains rent-seeking
In this section, we suggest an alternative control mechanism that MNC parents can use to
constrain the rent-seeking of their subsidiaries. We will first show that debt can be used as a
monitoring device. Then we will explain why short-term debt achieves this purpose better
than long-term debt. We will also discuss how parental guarantees affect the monitoring and
incentives of management of MNCs subsidiaries. We will then also explain why external debt
in the form of bank debt is an even better monitoring device. From these arguments, we will
derive testable hypotheses that we will test with UK data in the empirical part of our paper.
3.3.1 Use of debt as a monitoring device
In any firm where management and ownership are separated, (Jensen and Meckling, 1976)
showed that there are problems of misalignment of incentives between these two parties and
risks that the management will try to capture and use the corporate resources in order to
maximise their own benefit at the expense of the shareholders. (Jensen, 1986; Stulz, 1990;
Hart and Moore, 1995; Zwiebel, 1996; Morellec, 2004) asserted that one way to mitigate this
problem is by loading the firm with debt. Indeed, the higher the amount of debt, the higher the
amount of interest payments and, as a result, there would be a lower amount of free cash
flows available for management to misuse. Furthermore, given that interest payments have to
be met in order to avoid a situation whereby creditors can force the liquidation of the firm, the
taking up of debt would act as an incentive mechanism on management to work hard in order
to make sure that debt payments are met. (Chen et al., 1997) and (Chkir and Cosset, 2001)
made a similar argument in the context of MNCs. They suggested that the empirical evidence
of higher debt ratios observed in MNCs compared to domestic firms could be explained by
the fact that international operations are more difficult to monitor and MNCs may use higher
levels of debt for monitoring purposes.
3.3.2 Which form of debt ? Short-term or long-term debt?
Once that we have established that debt can serve as a monitoring and constraining device on
MNC subsidiary management, we will discuss which type between short-term or long-term
debt achieves this purpose more effectively. (Laux, 2001) and (Diamond, 2004) argue that
short-term debt is more effective in constraining management compared to using long-term
debt. Indeed, given that short-term debt has to be renewed often it is only this form of debt
that will provide management the highest amount of incentive to perform well. Indeed, if the
financing is renewed, the management will be able to continue benefiting from being
employed by subsidiary. On the other hand, if subsidiary financing is not renewed and the
subsidiary is liquidated then the association of the subsidiary management and the non–
renewability of finance will negatively impact their reputation in the managerial labour
market. Using short-term debt is the only way how the non-renewability of debt can act as a
credible threat on subsidiary management.
We derive our first testable hypothesis (H1): subsidiary firms will tend to have higher
amounts of short-term debt compared to equivalent domestic firms.
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3.3.3 Should we use MNC parent debt or external debt?
(Chowdhry and Nanda, 1994) discussed the differences and advantages of using parental or
external debt in financing MNCs subsidiaries. They argued that given that parental or internal
debt overcomes the problem of asymmetric information and bankruptcy costs, MNC
subsidiaries should exhibit higher amounts of internal debt. At the same time, the differences
in taxation rates between the home and host country would also influence the mix of internal
and external debt. For monitoring and constraining management purposes, we argue that
using external debt is preferable to using internal debt. Indeed, when the parent uses internal
debt, this could be considered as a sunk cost when deciding on renewing the subsidiary debt.
Indeed, following subsidiary management’s underperformance, the parent should not renew
the debt. But, in spite of the poor performance of subsidiary management, the parent could
still end up renewing the debt because the opportunity cost of not renewing the debt could
outweigh the benefits of renewing. This could be the case when the amount already spent is
either perceived as a sunk cost or the present outcome of the project still has a positive value
for the MNC as a whole. We argue that a similar situation will not happen if the subsidiary is
financed by external debt. Indeed, the latter would be re-paid in full in case of non-renewal
and the bank’s decision to renew would only be based on the subsidiary’s performance. Once
again the bank’s decision to renew the debt only based on performance would act as an
incentive on subsidiary management to perform well.
3.3.4 Using bank monitoring?
(Levi, 1996) suggested that the MNC parent should delegate some of its monitoring activities
to local financial institutions. (Ahn and Choi, 2009) used US data to study the association
between the extent of bank monitoring and the extent of the earnings management behaviour
of a borrowing firm. They provided evidence that the opportunistic reporting behaviour of
firm’s management was effectively constrained by bank monitoring activities. Following a
similar argument, (Krivogorsky et al., 2011) document that continental European firms
exhibit a better performance when these are financed through bank debt. Furthermore, (Du,
2003) argued that the involvement of banks, especially host country banks, hardens the
budget constrain on MNCs subsidiaries. The superiority of host country banks is based on two
facts. First of all, given that creditor and bankruptcy legislation tends to favour local creditors
compared to foreign creditors, host country banks are in a better position compared to foreign
or home country bank financing. In this regard, (Akbel and Schnitzer, 2011) document that
the monitoring power of banks will depend on the implementation of creditor rights in the
host country and on the unfolding of events following default. Secondly, banks will be more
prone to stop financing the bad-type of investment project as this will prove to be the least
costly option. Indeed, if the project financing is not renewed, the bank would in anyway get
repaid as most subsidiary debt is explicitly or implicitly guaranteed. Therefore, the only
reason for the bank to renew the project is that it is of the good-investment type. As a result,
the fact that the renewal of debt is only based on the actual performance of the MNC
subsidiary will act as an incentive for the MNC subsidiary management.
We derive our second testable hypothesis (H2): Subsidiary firms will tend to have higher
levels of short-term external (bank) debt compared to equivalent domestic firms.
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3.3.5 What is the effect of MNC parental guarantees?
In the context of MNC subsidiary financing, it is important to consider the issue of parental
guarantees given that even if they are not explicitly given they will be implicitly provided. In
fact, credit rating agencies, such as Standard & Poor’s, consider the relationship between
parents and subsidiary when determining the credit rating of the parent. The latter also
explains why parents will almost never let their subsidiaries go bankrupt as this might impact
their credit rating. One could argue that the effect of parental guarantees could potentially
remove the pressure on subsidiary management to perform well thinking that the project will
automatically be renewed. However, as we already demonstrated in section 3.3.4 the effect of
parental guarantees combined with bank monitoring will act as a credible deterrent to
underperform. Indeed, the combination of bank monitoring and parental guarantees does not
mean that the project will automatically be renewed as the bank has the option of either not
renewing the debt and getting re-paid through the exercise of the parental guarantee or
renewing the debt if the bank considers that the subsidiary performance was satisfactory.
3.3.6 Cultural Distance
There is an extensive literature that documents that the level of control exercised over MNC
subsidiary operations depends on the cultural distance between the home and host
location/distance (Barkema et al., 1996; Hamilton and Kashlak, 1999). The influence of
culture has also been documented by other authors ((Sekely and Collins, 1988) (Aggarwal and
Goodell, 2010)). Therefore, our empirical setting will also allow us to measure the
location/distance effect (see section 3.4). Indeed, by comparing UK and US subsidiary firms,
we are measuring the incremental effect of cultural distance on these firms leverage. We
predict that this effect should be small given that the US and UK tend to be classified in the
same group using various cultural measures, such as the (Hofstede, 1980; 1983; 1984; 1985)
measures.
3.4 Hypotheses to be tested
For each of the two hypotheses that we mentioned in sections 3.3.2 and 3.3.4, we will test
them using two samples as explained below to test two different effects.
Hypotheses 1 and 2 (subsidiary effect)1: These hypotheses will test the subsidiary effect by
comparing the financing arrangements between UK domestic and UK subsidiary firms. We
hypothesise that the financing of investment in UK subsidiaries of UK multinational
corporations (MNCs) (UK SUB) will tend to be done through short-term (H1), externallysourced debt (H2) compared to equivalent financing of similar UK domestic firms (UK
DOM).
Hypotheses 1a and 2a (location/distance/effect) 2 : These hypotheses will test the
location/distance effect by comparing the financing arrangements between UK subsidiary
firms of UK MNCs (UK SUB) and UK based subsidiaries of US MNCs (US SUB). We
hypothesise that the financing of investment in UK based subsidiaries of US MNCs (US
SUB) will tend to be done through short-term (H1a), externally-sourced debt (H2a) compared
to equivalent financing of similar UK subsidiary firms (UK SUB).
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4 RESEARCH DESIGN
In order to test our hypotheses, we selected the UK as our host country in which all our firms
will be based. In this framework, the control group consists of equivalent domestic firms for
hypothesis 1 and 1a (UK DOM) and equivalent domestic subsidiaries for hypothesis 2 and 2a
(UK SUB). The reason for opting to locate all our firms in one host country is that there is a
wide literature that documents that differences exists across countries in terms of various host
country-specific factors, such as institutional, legal, cultural and financial factors, for which
we would have to control (Bancel and Mittoo, 2004; La Porta et al., 1997; La Porta et al.,
1998; Demirguc-Kunt and Maksimovic 1999; 2002; Desai et al., 2004). According to this
literature, comparison and interpretation of financial data is rendered more difficult due to
differences in the accounting and disclosure practices. Furthermore, these studies have
documented that differences between countries in terms of type of legal system followed, the
extent of protection of creditors, the level of development of financial markets, the quality and
transparency of accounting standards, have an effect on financial policies. One could control
for these factors but given that the list of control variables mentioned above was not
exhaustive, we would run the risk of having confounding results by omitting possible
differences. Therefore, in order to make our hypothesis testing less complex and “cleaner” we
opted for one host country as we expect that these factors would equally affect all firms in
both of our hypotheses. Another valid reason why we restricted our empirical testing to one
host country is to eliminate further possible documented explanations of MNC subsidiaries
financing decisions. These are extensively discussed in section 6.2.
4.1 Choice of host and home country?
We chose to have the US as the home country for our MNCs subsidiaries and the UK as our
host country for various reasons. First, according to the US Bureau of Economic Analysis
(BEA), the UK was the first destination country for US MNCs subsidiaries in the world for
the last 93 years (period 2000-2009). The US BEA collects data on US MNCs activity and the
filing of data is an obligation under US legislation therefore, we expect those figures to be
accurate.
4.2 Econometric methodology
We used the same econometric methodology to test both hypotheses. We estimated the
following regression using an unbalanced panel data:
LEVit = δ 0 + δ 1 SUBit + δ2SIZE it + δ 3 PROFIT it + δ 4 GRWOPP it + δ 5 TAN it +
5
δ 6 NDTS it + δ 7 DIV + δ8 RISK it + δ9 AGE + ∑ δ10 INDUS it + γ i + Ε it
1
LEV stands for one of our four different measures of leverage. These are explained in section
4.3. We ran four different regressions for each hypothesis using, each time, one of our four
different leverage measures as our dependent variable.
SUB stands for subsidiary firm. This is a dummy equal to one if the firm is a subsidiary firm
otherwise equal to zero.
SIZE stands for firm size
PROFIT stands for firm profitability
GRWOPP stands for growth opportunities
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TAN stands for tangibility of assets
NDTS stands for non-debt tax shields
DIV stands for firm dividends
RISK stands for operational risk
AGE stands for years since incorporation date
INDUS stands for industry. These are dummies equal to one if the firm belongs to the
industry otherwise equal to zero. We have six different industries based on the UK SIC 2003.
The industries are AGR (agriculture, forestry fisheries (codes 1 to 5 inclusive), MIN
(Resource sector codes 10 to 14 inclusive), MAN (manufacturing codes 15 to 36 inclusive),
CON (Construction code 45), TRA (Trade codes 50 to 55), and SER (Services codes 64, 70 to
74, 80, 85, 91 to 93).
The subscript i stands for the ith firm and the subscript t stands for the year of data
γ i stands for unobservable firm effect
[Insert Table 1]
4.3 Description of variables
In this section we discuss the variables used in our empirical analysis.
4.3.1 Dependent variables
As mentioned in section 4.2, we are using four different measures of leverage to test our
hypotheses. The reason for having so many different measures of leverage is to render our
results more robust.
4.3.1.1 Total external debt (TOTEXTDEBT)
TOTEXTDEBT is the leverage measure that is the closest to what is traditionally used in the
literature, particularly capital structure literature. In fact, the predictions on the signs of the
coefficients for our independent variables (see table 1) would be expected to only apply to
this measure but not necessarily to the other measures of leverage used. Indeed, this measure
takes into account the total amount of debt compared to the other measures which are only
partial measures of debt.
4.3.1.2 Short-term debt (STDEBT)
This is a comprehensive measure of short-term debt as it includes all sources of short-term
debt. This measure is used to test hypotheses 1 and 1a. We would expect the SUB coefficient
to exhibit a positive relationship with STDEBT.
4.3.1.3 Difference between short-term and long-term debt (DIFFSTLTDEBT)
Another way how to test hypotheses 1 and 1a is to measure the relative use of short-term debt
compared to long-term debt. Our prediction is that subsidiary firms will tend to use more
short-term debt compared to long-term debt in both hypotheses 1 and 1a. Given that we are
deducting long-term debt from short-term debt we would expect the difference to be larger in
the case of subsidiaries, therefore we expect a positive coefficient for SUB.
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The reason why we cannot directly compare measures of short-term external debt between our
firms is that contrary to domestic firms 4 , subsidiary firms have access to internal capital
markets5. As a result, we cannot compare raw figures but have to use relative measures. The
use of internal capital markets for multinationals has been documented by (Desai et al., 2004)
and Aggarwal and Kyaw (2008). Both papers show that MNCs tend to make use of capital
markets, especially when the host country is deficient in terms of credit availability, political
risk, high inflation, and offers poor creditor protection.
4.4.1.4 Difference between short-term external and internal debt (DIFFSTLTEXTDEBT)
Based on the same reasoning as in the previous paragraph, we use a relative measure that
captures the relative use of short-term external debt compared to long-term external debt in
order to test hypotheses 2 and 2a. Our prediction is that subsidiary firms will tend to use more
short-term external debt compared to external long-term debt in both hypotheses 2 and 2a.
Given that we are deducting long-term external debt from short-term external debt we would
expect the difference to be larger in the case of subsidiaries, therefore we expect a positive
coefficient for SUB.
4.4.2 Independent and control variables
The control variables used in our econometric setting are all derived from the literature and
have been found in previous studies to significantly affect the amount of debt that a firm
borrows.
4.4.2.1 Size (SIZE)
Previous studies ((Mittoo and Zhang, 2008), (Dewaelheyns and Van Hulle, 2010)) document
that the larger the firm the higher it’s borrowing capacity therefore we should expect the SIZE
coefficient to be positively correlated with TOTEXTDEBT.
4.4.2.2 Profitability (PROFIT)
The literature has hypothesised two possible relationships between debt and profitability. On
the one hand, (Modigliani and Miller, 1963) explained that profitable firms might have higher
levels of debt in order to take advantage of tax shields. (Jensen, 1986) also hypothesised that
firms might issue more debt when they are not able to control the firm effectively. On the
other hand, the pecking order theory (Myers, 1984) and (Myers and Majluf, 1984) predicted
the opposite as issuing debt is a costlier option compared to using retained earnings due to
asymmetric information. In fact, most empirical evidence in the corporate finance literature
has found support for the pecking order theory (Rajan and Zingales ,1995; Booth et al., 2001;
Fan et al., 2012). However, in our case, given that subsidiary firms are mostly wholly owned
subsidiaries, we would not expect to have high levels of asymmetric information.
Furthermore, our argument is essentially similar to (Jensen’s, 1986) therefore we predict that
firms with higher profitability will be able to take on more debt thus we expect a positive
relationship between TOTEXTDEBT and PROFIT.
4.4.2.3 Growth opportunities (GRWOPP)
The relationship between debt and growth opportunities could be twofold (Mittoo and Zhang,
2008; Dewaelheyns and Van Hulle, 2010). On the one hand, firms with more growth
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opportunities cold use debt to finance these opportunities and would thus be expected to have
higher amounts of debt. In that case, we would also expect a positive relationship between
debt and growth opportunities. On the other hand, due to asymmetric information and moral
hazard, growth could imply lower debt levels. Given that subsidiary firms are wholly owned
subsidiaries and that such firms tend to have an implicit, if not explicit, guarantee on their
debt from the parent company therefore we would expect the former effect to be prevalent and
thus predict a positive relationship between TOTEXTDEBT and GRWOPP.
4.4.2.4 Tangibility (TAN)
(Aggarwal and Kyaw, 2010; Mittoo and Zhang, 2008) showed that the higher the amount of
tangible assets, the higher the liquidation value of the firm in case of bankruptcy. Therefore,
creditors are more willing to lend to firms with higher amounts of tangible assets, therefore
we predict a positive coefficient for TAN.
4.4.2.5 Non-debt tax shield (NDTS)
(Jensen et al., 1992; Mittoo and Zhang, 2008; Kwok and Reeb, 2000) document that firms can
also use non-debt tax shields (NDTS) in order to reduce their taxation on profits. Firms using
NDTS would relatively need lower levels of debt therefore we expect a negative sign between
TOTEXTDBT and NDTS.
4.4.2.6 Dividends (DIV)
(Aggarwal and Kyaw, 2010; Desai et al., 2007) found that dividend policy influenced
leverage in multinational firms. In the case of dividends, the expected coefficient could be
either positive or negative. On the one hand, higher dividends could signal higher and more
stable profitability therefore re-assuring creditors which would be willing to lend higher
amounts of debt. In this scenario, we would expect the coefficient DIV to be positive. On the
other hand, if the firm decides to finance investment internally, they would pay lower
dividends and would require less debt. In this case, we would expect a negative relationship
between TOTEXTDEBT and DIV.
4.4.2.7 Risk (RISK)
(Doukas and Pantzalis, 2003; Mittoo and Zhang, 2008) document that debt levels are
influenced by the firm’s risk. We are using the QUI score as a risk measure. The score can
take on values from 0 to 100. The higher the score the lower the credit risk therefore we
would expect a negative relationship between TOTEXTDEBT and RISK.
4.4.2.8 Age (AGE)
We are computing the age by using the difference between the incorporation date and the year
of the data we are using. The expected sign of the AGE coefficient could be either positive or
negative (Dewaelheyns and Van Hulle, 2010). On the one hand, we could consider that the
longer the existence of the firm, the higher the opportunity to accumulate profits and therefore
use retained earnings to finance investment. This would mean that as the age increases the
reliance on debt diminishes thus suggesting a negative relationship between AGE and
TOTEXTDEBT. (Hall et al., 2000) suggests that this might, particularly, be the case for
private firms. On the other hand, as the firm ages, it would become more established and have
12
a longer track record which could be used to re-assure creditors. In this case, we would expect
a positive relationship between AGE and TOTEXTDEBT (Petersen and Rajan, 1997). Given
that our subsidiaries are mostly wholly owned firms, we would expect them to rely more on
retained earnings to finance investment as they age and thus predict a negative relationship
between AGE and TOTEXTDEBT.
4.4.2.9 Industry (MIN, MAN, BUIL, TRA, and SER)
(Myers, 1984; Harris & Raviv, 1991) provided evidence that the industry in which a firm
operates is a significant factor that influences the amount of debt that a firm can borrow.
Therefore, we expect our industry dummies to be significant.
4.5 Data and sample selection
We used Fame database in order to select the samples. Fame database (Bureau Van Dijk)
provides detailed financial information on UK and Irish Companies based on original filings
of companies with the UK’s Companies House and Companies Registration Office in Ireland.
4.5.1 Definition of domestic firm and subsidiary firm
We used the standard definitions used in the literature to define domestic and subsidiary
firms. A domestic firm is one which only operates in one country and does not have overseas
turnover and is not owned by another firm. A subsidiary firm is defined as a firm that is
owned by another firm. In our case, UK subsidiaries were defined as being ultimately owned
by another UK firm and US subsidiaries were defined as being ultimately owned by a US
firm.
4.5.2 Download criteria used for all three samples
We downloaded three different samples for our UK domestic firms (UK DOM), UK
subsidiaries (UK SUB) and UK subsidiaries of US MNCS (US SUB) using several criteria as
per previous literature. First, we required all firms to be active, be registered in the UK, be
directly majority owned, and to have at least 10 years of accounts data available (Our data
covers the period 2001 to 2010). Second, in order to avoid having firms which might be set up
for other non-trading purposes, we excluded all firms which had less than 9 employees. Third,
we excluded firms operating in the financial services, utilities, public administration, and
defence sectors because these might have special requirements in terms of leverage.
Furthermore, we used additional criteria specific to each one of our samples. In the case of
UK domestic firms, firms had to be an ultimate holding company located in the UK and could
not be a subsidiary firm. In the case of UK subsidiary firms, firms could not have subsidiaries,
had to have 1 shareholder and had to be ultimately owned by an industrial shareholder from
the UK owning more than 50.01%. Finally, similarly to our UK subsidiary firms, US
subsidiary firms could not have subsidiaries, had to have 1 shareholder and had to be
ultimately owned by an industrial shareholder from the US owning more than 50.01%.
Furthermore, following standard practice in the literature, we performed additional checks and
eliminated firms with negative equity and missing data. In the case of UK domestic firms, we
eliminated firms with overseas sales and those which had group loans (either short-term or
long-term).
13
4.5.3 Final size of samples
For UK domestic firms, from an original sample of 3004 firms we ended up having a total of
6283 firm-year observations with the number of firms per year varying from a low of 200 in
2001 to a maximum of 860 in 2010.
For UK subsidiary firms, from an original sample of 1276 firms we ended up having a total of
5479 firm-year observations with the number of firms per year varying from a low of 177 in
2001 to a maximum of 746 in 2009.
For US subsidiaries, from an original sample of 1030 firms we ended up having a total of
4617 firm-year observations with the number of firms per year varying from a low of 139 in
2001 to a maximum of 624 in 2009.
However, from table 2 one can notice that for all our samples, each year approximately
corresponds to 10% of our total observations except year 2001 (lower with 3%) and years
2009-2010 for UK domestic and years 2008-09 for UK and US subsidiaries (higher with 13%
and 14%). However, these variations are common and similar to all our three samples
therefore we should not expect them to have an overall impact on our results.
[Insert Table 2]
As a result the total size of our sample for hypotheses 1 and 1a (subsidiary effect) is 11762
firm-year observations while the total size of our sample for hypotheses 2 and 2a
(location/distance effect) is 10096 firm-year observations.
4.6 Sample characteristics
[Insert Table 3]
Table 3 provides the descriptive statistics of our three samples. As expected, mean and
median short-term debt (STDEBT) is higher for both our subsidiaries compared to UK
domestic firms and also higher for US subsidiaries compared to UK subsidiaries. The mean
and median figures for DIFFSTLTDEBT also confirm that our subsidiaries tend to use more
short-term debt compared to long-term debt. This assertion also applies for our subsidiaries as
mean and median DIFFSTLTDEBT are higher for our US subsidiaries compared to UK
subsidiaries. In the case of DIFFSTLTEXTDEBT, the picture is slightly different as our
subsidiary firms both have higher levels of short-term external debt but when we compare UK
and US subsidiary firms, the former have higher levels of short-term external debt.
With regards to our control variables, the figures show that our UK domestic firms are the
largest followed by US subsidiaries. Profitability is highest for UK domestic firms followed
by US subsidiaries. Growth opportunities are the highest for UK domestic firms followed by
UK subsidiaries. The same ranking also applies to tangibility and non-debt tax shields.
Dividends are highest for US subsidiaries followed by UK subsidiaries. In terms of risk, US
firms have the lowest credit rating followed by UK subsidiaries. With regards to the age of
the firms, our US subsidiaries have the shortest existence with the UK subsidiaries being
marginally older than UK domestic.
14
5 RESULTS FULL SAMPLE
This section reports the results of our regressions for both hypotheses when using our full
sample. All our regressions’ standard errors were corrected for heteroscedasticity. As part of
our diagnostic tests, we also checked the correlation between two or more of the independent
variables. For this purpose, we used the variance inflation factor (VIF). Although the
literature does not have a consensus figure on an acceptable VIF-size, authors have suggested
5 or even figures below 10 as acceptable. In our case, all the dependent variables had figures
below 5 except three of the industry dummies which had VIFs higher than 10. As a result, we
can conclude that multicollinearity is not an issue for the estimates of our model.
5.1 Results hypotheses 1 and 2 (subsidiary effect)
[Insert Table 4]
For hypothesis 1, we are comparing the UK domestic with UK subsidiary firms. With regards
to the SUB coefficient, it is highly significant for all our specifications. The only two that
matters are the STDEBT and the DIFFSTLTDEBT. As hypothesized, the signs of the
coefficients are both as predicted and highly significant (1%). The results show that our
subsidiaries have 9.80% more short-term debt compared to domestic firms. With regards to
DIFFSTLTDEBT, our findings indicate that it is 14.46% lower in the case of subsidiary
firms. These results are economically significant and confirm that our subsidiaries have a
higher tendency to use short-term debt compared to long-term debt.
With regards to hypothesis 2, we also report a positive and highly significant coefficient for
SUB as predicted. The results show that our subsidiary firms have an additional 1.9% shortterm external debt.
Although the results that we found are significant by themselves, we can say that they are
even more important given that we have controlled for all other factors usually used in the
literature to explain differences in leverage. We use the results of the regression with
TOTEXTDEBT as our dependent variable in order to analyse the coefficients of our
independent variables. The majority of our control variables are significant and of the
expected sign except for size. The reason for the latter’s difference might be that our
definition of leverage is not exactly the same as the one used in the literature.
5.2 Results hypotheses 1a and 2a (location/distance effect)
[Insert Table 5]
For hypothesis 1a, we are comparing UK domestic firms with US subsidiary firms. With
regards to the SUB coefficient, it is highly significant for all our regressions except when we
use STDEBT in which case it is only significant at the 10% level. When we look at
hypothesis 1a the coefficients are of the expected sign when we use STDEBT and
DIFFSTLTDEBT. However, our US subsidiaries have on average less than 1% (0.7%)
additional short-term debt compared to our UK subsidiaries. In the case of the measure
DIFFSTLTDEBT the difference is 2.65% more for our US subsidiaries. This confirms that
our US subsidiaries tend to be more financed with short-term debt compared to our UK
subsidiaries. However, the marginal difference could be explained by the low cultural
15
distance between the US and the UK. Some authors have also found that MNCs subsidiaries
tend to conform to similar levels of leverage of equivalent domestic firms.
For hypothesis 2a, our results are highly significant but not of the expected sign. This shows
that our UK subsidiary firms have an additional 0.5% external short –term debt.
We compare the coefficients of our control variables with previous studies using the
TOTEXTDEBT measure as dependent variable given that it is the closest to the leverage
measures used. The majority of our coefficients are highly significant and of the expected
sign. GRWOPP is of a different sign compared to hypothesis 1 but it is not significant.
6 ROBUSTNESS CHECKS
6.1 Matched sample
One might argue that given the difference in the characteristics of our firms in our samples,
such as the number of firms and number per industry, our results could be a result of sample
bias. To check that our results are not driven by the differences in our samples, we construct a
matched sample in order to control for sample bias. We construct our matched sample
following standard procedure in the literature, namely following (Mittoo and Zhang, 2008;
Dewaelheyns and Van Hulle, 2010). First, we match our firms by year, followed by industry
(using the UK SIC (2003) industry code), and by size of the firm. We select the firm which
has the closest size to our UK subsidiary for the subsidiary hypothesis (1) and the firm which
has the closest size to our US subsidiary in the case of the location/distance hypothesis (2).
When we do this we end up with a sample of 5670 firm year observations for hypothesis 1
compared to 11762 firms in our original sample. For hypothesis 2 we end up with a sample of
3896 firms compared to 10096 in our original sample.
[Insert Table 6]
Table 6 gives a breakdown by industry and in percentage terms of the composition of our full
and equivalent matched samples for both the subsidiary and location/distance effects. One can
notice that in terms of industry breakdown both samples for both hypotheses are virtually the
same. The only notable difference that we can notice is that in the case of the
location/distance effect the percentage of firms in the building industry drops down from 9%
to 2%. This can be explained by the fact that, as expected, a very small number of US
subsidiaries operate in the building industry. Therefore there were only a small number of
firms in this industry that could be matched. Overall, we can say that given the very close
similarity between our matched and full samples, if our results are confirmed by the results of
our matched samples then we could consider them as being robust and not subject to sample
bias.
[Insert Table 7]
Table 7 provides us the descriptive statistics of the UK domestic firms’ full sample and
equivalent matched sample. We notice that for all our variables the figures are very close in
both samples.
[Insert Table 8]
16
Table 8 reports the descriptive statistics of the UK subsidiaries samples. In this case we have
one full sample and two matched samples as we had to match them with the UK domestic
firms and US subsidiary firms. All three samples are fairly similar except for DIV and
GRWOPP where the differences are more pronounced.
[Insert Table 9]
Table 9 shows the descriptive statistics of the US subsidiary firms for the full and matched
sample. The figures are very close to each other except for the variables PROFIT, GRWOPP
and DIV.
Out of the three groups of firms, the lowest differences between the matched and full sample
is in the case of US subsidiaries. Overall, in all three firm groups, the differences are marginal
therefore if we had to find that our full sample results are confirmed in the case of our
matched samples then this would render our results robust.
6.1.1 Results matched sample
6.1.1.1 Results hypotheses 1 and 2 (subsidiary effect)
[Insert Table 10]
Table 10 reports the results for hypothesis 1 (subsidiary effect) for our matched sample. When
comparing these results with those obtained for our equivalent full sample (Table 4) we
conclude that our results do not change. The SUB coefficient is highly significant for all our
specifications and the signs of the coefficient are the same. Furthermore, the results show that
the economic significance of our results is even higher. Whereas in the case of our full
sample, our subsidiaries had 9.80% additional short-term debt, when taking the matched
subsidiary effect sample, our subsidiary firms have an additional 11.35% short-term debt.
The same applies when we compare the SUB coefficients for the DIFFSTLTDEBT column.
In this case the difference increases from 14.46% to 16.27%. This confirms that our
subsidiaries have a higher tendency to use short-term debt compared to long-term debt even
for the firms in our matched sample.
In the case of hypothesis 2, our results are still positive and highly significant but the
economic significance is lower as our subsidiary have an additional 1.4% short-term external
debt compared to 1.9% when considering the full sample.
With regards to the control variables, our results are slightly different when using the matched
sample. The coefficients SIZE, PROFIT, and DIV are not any more significant while
GRWOPP has become significant. Some of the industry dummies also cease to be significant.
However, the signs of the control variables are as expected.
6.1.1.2 Results hypotheses 1a and 2a (location/distance effect)
[Insert Table 11]
Table 11 presents the results for hypotheses 1a and 2a (location/distance effect) for our
matched sample. We obtain the same results as those obtained for our full sample (Table 5) in
terms of signs of coefficients and significance level for our SUB coefficients. Furthermore,
17
whereas the SUB coefficient when using STDEBT was only marginally significant for our
full sample, in the case of the matched sample the coefficient is now highly significant.
Moreover, the economic significance has doubled increasing from 0.7% to 1.56% more shortterm debt for our US subsidiary firms compared to UK subsidiary firms. The same conclusion
applies when we consider the results using DIFFSTLTDEBT as the coefficient has increased
from 2.65% to 3.98%. Therefore, we can confirm that US subsidiaries are more financed with
short-term debt compared to our UK subsidiaries.
When considering hypothesis 2a, our results are still highly significant and of the same sign as
for the full sample. The only difference is that the economic significance has increased. These
results are not as predicted as our UK subsidiary firms have more short-term external debt
compared to our US subsidiary firms.
With regards to the control variables, the signs of the coefficients are the same as in the case
of the full sample. Furthermore, NDTS has now become marginally significant, while DIV
has become significant, and AGE has dropped from being highly significant to being
marginally significant. The signs of the coefficients are as predicted except for GRWOPP but
the latter is not significant.
6.2 Alternative explanations for our results
This section discusses possible alternative explanations for our subsidiary firms using more
short-term and more short-term external debt compared to our domestic firms.
6.2.1 Use of unconsolidated accounts
The use of unconsolidated accounts by the MNC parent firm could explain the incidence of
higher amounts of debt in our subsidiary firms. Indeed, MNC parents filing unconsolidated
accounts could load their subsidiaries with debt, as that debt would not appear on their
accounts thus benefitting from lower leverage ratios and hence giving the impression of a
lower credit risk to their lenders. However, this argument would apply using the total amount
of the subsidiary debt. However, our argument is not about the total amount of debt but rather
on the preference of short-term and short-term external debt rather than total debt. From the
perspective of presenting accounts, the choice of source, whether internal or external debt,
and the choice of maturity, whether short-term or long-term, should not have any impact
given that the total amount of debt would not be affected. It is the latter that matters when the
parent firm wants to hide debt and appear less leveraged. Furthermore, since January 2008,
following the introduction of a revised version of IAS 27 (International Accounting
Standards)6 concerning consolidated and separate financial statements, firms with subsidiaries
cannot anymore make use of unconsolidated accounts. In other words, all firms would have to
report the total amount of debt even debt which is contracted by subsidiaries. As a result, we
are confident that we can rule out the use of unconsolidated accounts by the parent MNC as a
possible explanation for our differences in leverage.
6.2.2 Taxation
The effect of taxation on MNCs capital structure has been investigated by several authors
(Desai et al., 2004; Huizinga et al., 2008; Arena and Roper, 2010; Egger et al., 2010). Given
that interest expense is tax deductible, the different amounts of debt between domestic and
subsidiaries could be explained by corporate tax rate differentials. Indeed, firms with multiple
18
subsidiaries could decide to locate debt in jurisdictions where the tax rate is higher in order to
reduce their overall taxation. However, this argument would certainly not apply in the case of
our subsidiary effect hypothesis given that we are comparing UK domestic and UK subsidiary
firms which are all based in the same country and therefore subject to the same taxation
regime.
When considering the location/distance effect, it is possible that the difference between the
US and UK taxation rates could explain our results. However, for the entire period considered
in our study (2001-2010), corporate tax rates were higher in the US compared to the UK thus
suggesting that debt should be located in the US rather than the UK to maximise the benefits
of interest rate deductibility. Therefore, even though the taxation argument suggests that debt
levels should be lower for our US subsidiaries compared to UK subsidiaries, we found the
opposite results. Furthermore, the economic significance of our results was higher in the case
of the subsidiary effect, where there is no taxation rate differential between our firms,
compared to the location/distance effect. As a result, we conclude as (Acharya et al., 2011),
who found similar results, that taxation is not a likely explanation.
6.2.3 Exchange controls and other restrictions
(Errunza, 1979) have documented that the imposition of exchange control restrictions from
the host government will influence both the MNC parent decision where to source funds and
what type of funds to use to finance the subsidiary. If exchange control restrictions are
imposed, the MNC parent will tend to prefer to source funds from the host country since it
will not have problems of repatriating funds originally invested. Furthermore, host
governments could impose different exchange control restrictions depending on the type of
funds being exported. For example, dividends based on declared after-tax profits might be
subject to lower restrictions than intercompany loans and intercompany interest which is more
subject to manipulation by both the MNC parent and subsidiary. In the case of the UK, all
foreign exchange controls have been abolished in 1979. Therefore, we can reasonably state
that we do not expect MNC subsidiary financing decisions to be influenced by foreign
exchange control restrictions. Likewise, there are no FDI restrictions from the US to the UK
market. As a result, we can also rule out exchange control considerations as a possible source
of influence for our MNCs subsidiary financing decisions.
6.2.4 Exchange rate risk
(Desai et al., 2008) have documented that exchange rate fluctuations between host and home
currencies, particularly when these are significant, such as when there is an expectation of
devaluation, could influence the choice of currency and possibly the market where the funds
are sourced. However, (Lehmann et al., 2004), investigating the determinants of financing
choices of MNCs subsidiaries in 53 countries over the period 1983 to 2001, showed that, over
time, exchange rate risk became less relevant in determining MNC subsidiary financing
choices as exchange rate fluctuations decreased and, at the same time, more advanced hedging
techniques became available to MNCs subsidiaries. We can also add that in the case of the
pound sterling and US dollar, there have not been any devaluations or revaluations since the
pound sterling devaluation of September 1992. Moreover, we can also mention that
companies can mitigate any negative effects due to exchange rate fluctuations by using
exchange rate hedging instruments. While we acknowledge that the market for forward
exchange rate might not exist for each currency pair and, even for pairs that exist, that it
might not be liquid at all times (especially when devaluations are expected), statistics show
19
that the US dollar/British pound pair is one of the most traded. As a result, we can reasonably
assert that exchange rate fluctuations are not expected to be the major factor which influences
US MNCs subsidiaries financing decisions.
6.2.5 Political and expropriation risk
(Levi, 1996; Kesternich and Schnitzer, 2010) mention political and expropriation risk as
possible factors influencing the choice of financing of MNCs subsidiaries. In the presence of
political and expropriation risk, MNCs tend to source and locate debt in the country with the
highest risk. According to Euromoney (2011), the US had an ECR7 score of 82.1 (Euromoney
March 2011) while the UK had an ECR score of 80.22 (Euromoney March 2011).
Euromoney’s ECR score ranges from 0 to 100 with 100 representing no country risk. It is also
sub-divided in 5 tiers with scores ranging from 80 to 100 being the top tier and classified as
low country risk. When we compare the country risk measures between the UK and the US,
we only find a marginal difference but both countries are classified in the top tier considered
to be very low risk. Therefore we expect that country risk is considered non-existent and thus
will not influence the MNC subsidiary financing decision. While this factor does certainly not
explain the difference in leverage between our UK domestic and UK subsidiary firms, it could
explain the marginally higher leverage of our US subsidiaries compared to our UK subsidiary
firms. In terms of expropriation risk, we can also say that this is equal to zero.
6.2.6 Availability and access to capital markets
Another possible alternative explanation for our differences in the leverage levels between our
domestic firms and subsidiaries could be differences or restrictions in terms of availability of
finance in both the home and host country capital markets. Once again, this argument would
not explain the difference in leverage levels in the case of our subsidiary hypothesis. Thus, we
only consider it as a possible explanation for our location/distance hypothesis. (Stobaugh,
1970; Robbins and Stobaugh, 1972) explained that the apparent low use of the external capital
markets in both the home and host country could be linked to the level of institutional, legal
and financial protection provided, besides these markets being existent, developed, and liquid.
(Desai et al., 2004; Aulakh and Mudambi, 2005) found evidence that MNCs make use of
internal capital markets opportunistically as they substitute for external capital markets
whenever these are weak in the host or home country or whenever the host country has weak
creditor rights. Given that the US and UK capital markets are respectively the largest and
second largest (Bank for International Settlements) in the world and that the level of external
capital markets development and debt-finance availability is not weaker in the US compared
to the UK, we believe that these capital markets are highly liquid and deep, thus there are no
restrictions on accessibility. Moreover, this argument would explain the higher incidence of
use of total debt whereas our argument concerns the relative use of short-term debt and the
source of short-term debt. Therefore, we can reasonably rule out that our differences in
leverage levels are explained by differences in terms of capital market accessibility.
6.2.7 Institutional and Legal factors
(Mishra and Tannous, 2010) showed the impact of differences in securities laws in host
countries on the capital structure of US multinationals while (Acharya et al., 2011)
investigated the role of bankruptcy codes on capital structure choices. (La Porta et al., 1997;
1998; 2006; Alves and Ferreira, 2011) also provided evidence that institutional and legal
20
factors affect capital structures. In the latter’s papers, the UK and US rank similarly in terms
of the various institutional and legal factors. Although the rankings of these two countries are
not exactly the same they are always ranked in the same group with a slightly higher ranking
for the UK. The latter means that the UK is seen as a lower risk and thus would encourage the
use of higher levels of debt. Furthermore, the group of the UK and US is always ranked the
highest, especially when it refers to access to external finance and creditors rights protection,
two important factors when discussing financing of MNCs subsidiaries. They also have the
same type of legislative system (common-law) and a high degree of investor protection. Once
again, these differences do not matter for the subsidiary effect given that all firms are UK
firms. In the case of the location/distance effect, given that both countries share fairly similar
institutional and legal factors, we do not expect our results to be driven by such factors.
7 CONCLUSION
In this paper, we investigate two hypotheses regarding the use of different financial
contracting arrangements to finance subsidiary firms in order to constrain the rent-seeking
behaviour of subsidiary management. One hypothesis analyses the subsidiary effect by
comparing the use of short-term and short-term external debt between UK domestic and UK
subsidiary firms. The second hypothesis analyses the location/distance effect by comparing
the use of short-term and short-term external debt between UK and US subsidiary firms based
in the UK. Using an unbalanced panel data of 11762 and 10096 firm-year observations we
find that UK subsidiaries have more short-term debt and more short-term external debt
compared to equivalent UK domestic firms and that US subsidiaries have more short-term
debt and less short-term external debt compared to equivalent UK subsidiaries. Our results are
both statistically and economically significant. We find these results in spite of our domestic
firms being larger, being more profitable, having higher growth opportunities, and having
higher tangibility, all factors which should translate in higher amounts of debt. Our results are
robust, even after controlling for all the variables that have been found in the literature to
affect leverage, and when we used a matched sample approach.
Our results are limited to the UK and US but we deliberately chose to restrict our set-up to
two countries. However, one could think of extending this work to a multi-country framework
and investigate whether these results could also be generalised. In such case, we would have
to control for the standard institutional, legal, and cultural factors that have been used in the
literature when making international studies. Furthermore, another possible extension of this
work could be to analyse whether the use of short-term debt and external debt is positively
related to the bargaining power of the MNC subsidiary. Indeed, we would expect that the
higher the bargaining power of the subsidiary management, the higher the use of this
mechanism to restrain the rent-seeking behaviour of the MNC subsidiary. Another possible
extension is to investigate the lower amounts of short-term external debt found when
comparing UK and US subsidiary firms.
In terms of implications for practitioners, our paper has provided with MNC parents an
additional tool that can be used in order to control the behaviour of their subsidiaries. The
advantage of this tool over others is that it might be easier to implement.
21
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26
Table 1: Name of Variables, Formula to compute, source of data, variable(s) used in database, and expected sign of coefficient
This table defines the variables used in our regressions as well as showing the formula used to compute each one of the variables. It also
provides the source of data together with the names of the variables used in the source database. This table also provides the expected sign
of the coefficient of the relevant independent variables. These expected signs are only relevant when using TOTEXTDEBT as a dependent
variable.
Variable Name
Formula to Compute variable
Source of Data
Variable(s) used in database
TOTEXTDEBT
(Bank overdraft + Other Long-term
Debt)/(Current + Long-term liabilities)
Fame Database
Bank overdraft,other long-term loans,
total assets, and total assets-liabilities
STDEBT
Short-term loans & overdrafts/(Current +
Long-term liabilities)
Fame Database
short-term loans & overdrafts, total
assets, and total assets-liabilities
DIFFSTLTDEBT
(Short-term loans & overdrafts-Long-term
debt)/(Current + Long-term liabilities)
Fame Database
long-term debt, short-term loans &
overdrafts, total assets, and total
assets-liabilities
DIFFSTLTEXTDEBT
(Bank overdrafts-other long-term debt)/LongFame Database
term liabilities
Expected sign of
coefficient
Dependent Variable
LEV category
Bank overdraft,other long-term debt,
total assets, and total assets-liabilities
Independent and control variables
SUB
Dummy variable
Fame Database
Determined by author
+
SIZE
Log of sales
Fame Database
Turnover
+
+
PROFIT
EBIT/Sales
Fame Database
Earnings before Interest and Taxes
and Turnover
GRWOPP
(Salest - Salest-1)/Salest-
Fame Database
Turnovert and Turnovert-1
+
TAN
(Property + Plant and Equipment)/Total Assets Fame Database
Land and Buildings, Plant & Vehicles,
and Total Assets
+
NDTS
(Depreciation + Amortization)/Total Assets
Fame Database
Depreciation, Amortization and Total
Assets
-
DIV
Dividends
Fame Database
Dividends
+/-
RISK
QUI Score
Fame Database
QUI Score
+
AGE
Year of data - year of incorporation date
Fame Database
Incorporation date
MIN
Dummy variable
Fame Database
UK Primary SIC Code (2003)
MAN
Dummy variable
Fame Database
UK Primary SIC Code (2003)
BUIL
Dummy variable
Fame Database
UK Primary SIC Code (2003)
TRA
Dummy variable
Fame Database
UK Primary SIC Code (2003)
SER
Dummy variable
Fame Database
UK Primary SIC Code (2003)
+/-
27
Table 2: Number of observations per year for each sample
This table shows the number of observations that we have for each year
for each of our three samples. The data is also provided on a percentage
basis.
UK domestic
UK subsidiaries
US Subsidiaries
Number
%
Number
%
Number
%
2001
200
3%
177
3%
139
3%
2002
559
9%
466
9%
398
9%
2003
620
10%
507
9%
447
10%
2004
617
10%
542
10%
469
10%
2005
635
10%
541
10%
476
10%
2006
625
10%
580
11%
517
11%
2007
642
10%
658
12%
549
12%
2008
691
11%
706
13%
592
13%
2009
834
13%
746
14%
624
14%
2010
860
14%
556
10%
406
9%
Total
6283
100%
5479
100%
4617
100%
28
Table 3: Descriptive statistics for UK domestic, UK subsidiaries, and US subsidiaries
This table shows the descriptive statistics for our three samples. The variables shown in the first column are
described in Table 1.
Variable
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
SIZE
PROFIT
GRWOPP
TAN
NDTS
DIV
RISK
AGE
Nbr of
Sample Mean
observations
Median Std. Dev. Minimum Maximum
6283
UK dom 0.2606941 0.1737198 0.2778187
0
0.9972731
5479
UK sub
0.0707192
0
0.1512614
0
0.9855772
4617
US sub
0.036773
0
0.1119782
0
0.8453054
6283
UK dom
0.17041
0.1013279 0.1981579
0
0.9972731
5479
UK sub
0.288567 0.2034801 0.2819823
0
1
4617
US sub
0.3014235 0.2373723 0.2742961
0
1
6283
UK dom -0.0045842
5479
UK sub
0.2100224 0.1502665 0.3679089
-1
1
4617
US sub
0.2392496 0.1947337 0.3488389
-1
1
6283
UK dom -0.044058
0
0.29379233 -0.9757643
0.9972731
5479
UK sub
0.0267269
0
0.14689348 -0.8938703
0.9855772
4617
US sub
0.0152688
0
0.1081281 -0.8371585
0.8453054
6283
UK dom
9.476891 9.5090366
1.423838
1.178655
16.04907
5479
UK sub
9.190595 9.1892189
1.372842
0.9166906
14.19755
4617
US sub
9.232716 9.1851811
1.24416
0.6931472
13.69335
6283
UK dom
1989.957
17400
-218252
689000
5479
UK sub
0.0773198 0.0489649 0.3126372
-12.27576
5.064006
4617
US sub
0.2230337 0.0612284
7.907892
-6.761898
529
6283
UK dom
0.0573375
180.3983
-0.9949051
11647.46
5479
UK sub
0.4418168 0.0535239
7.836428
-0.9924064
354.1754
4617
US sub
0.7706488 0.052144
28.86967
-39.42435
1910
6283
UK dom 0.2616953 0.167259
0.2720402
0
1.105989
5479
UK sub
0.1485836 0.0342793 0.2338032
0
0.997268
4617
US sub
0.0932316 0.0244659 0.1486944
0
1
6283
UK dom 0.0307753 0.020767
5479
UK sub
0.0522035 0.0199544 0.3790334
0
16
4617
US sub
0.0311415 0.0191213 0.0417209
0
0.8811172
6283
UK dom
275.7298
0
2108.949
0
96000
5479
UK sub
538.4337
0
2781.445
0
107600
4617
US sub
548.6533
0
3588.269
0
135500
6283
UK dom
85.08356
91
14.62327
6
96
5479
UK sub
83.28308
90
15.72818
1
96
4617
US sub
82.5822
90
15.89905
1
96
6283
UK dom
21.0347
16
17.98707
0
131
5479
UK sub
21.1973
16
18.66869
1
144
4617
US sub
17.75417
14
14.86483
1
94
5.36974
0
549.398
0.3191098 -0.9739591
0.0368714 -0.0720631
0.9972731
1.069625
29
Table 4: Subsidiary Effect - Full Sample
This table provides the estimates of an unbalanced panel data regression model for the 11762 firm-year observations
using UK domestic and UK subsidiaries data. The variables used as column headings are the dependent variables
used while the variables shown in the first column are our independent variables. All the variables aredefined in
Table 1.
CONS is our constant and N the total number of observations. All our standard errors are corrected for
heteroscedasticity. The standard errors are shown in parentheses below coefficient estimates. ***, **, and *
indicatesignificance at 1%, 5%, and 10% respectively.
Hypothesis 1
SUB
SIZE
PROFIT
GRWOPP
TAN
NDTS
DIV
RISK
AGE
CONS
Hypothesis 2
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
-0.1761289***
0.0979638***
0.1446111***
0.019295***
(0.0013017)
(0.0023519)
(0.0025067)
(0.0006111)
-0.0011784***
-0.0214886***
0.0191903***
-0.0016745***
(0.0003695)
(0.0007762)
(0.0004146)
(0 .0002268)
-0.000000258*
0.000000138***
1.20E-07
-0.000000639***
(0.00000014)
(0.0000000427)
(0.000000166)
(0.0000000995)
0.0000116
-0.0000118*
0.0000625***
-0.0000958***
(0.000027)
(0.00000649)
(0.00000635)
(0.0000118)
0.2705757***
0.0664517***
0.3005892***
-0.2304706***
(0.0036022)
(0.0026454)
(0.0034776)
(0.0023323)
-0.0131077***
-0.0210897***
0.0383529***
0.006036
(0.0050747)
(0.0038608)
(0.0083532)
(0.0036477)
0.00000037***
-0.00000106**
-1.20E-07
-3.32E-07
(0.000000133)
(0.000000509)
(0.000000561)
(0.000000178)
-0.0002219***
-0.0011168***
0.0010959***
-0.000827***
(0.0000334)
(0.0000531)
(0.0000433)
(0.0000238)
-0.000176***
0.0000447
-0.000736***
0.0002663***
(0.0000241)
(0.0000354)
(0.0000367)
(0.0000146)
0.2479455***
0.5054274***
-0.4249308***
0.17906***
(0.0107772)
(0.0087566)
(0.011542)
(0.0091591)
INDUSTRY Dummies
Yes
Yes
Yes
Yes
YEAR Dummies
Yes
Yes
Yes
Yes
11762
11762
11762
11762
N
The standard errors are shown in parentheses below coefficient estimates.
***, **, and * indicate significance at 1%, 5%, and 10% respectively.
30
Table 5: Location Effect - Full Sample
This table provides the estimates of an unbalanced panel data regression model for the 10096 firm-year
observations using UK and US subsidiaries data. The variables used as column headings are the dependent
variables used while the variables shown in the first column are our independent variables. All the variables are
defined in Table 1.
CONS is our constant and N the total number of observations. All our standard errors are corrected for
heteroscedasticity. The standard errors are shown in parentheses below coefficient estimates. ***, **, and *
indicate significance at 1%, 5%, and 10% respectively.
Hypothesis 1a
SUB
SIZE
Hypothesis 2a
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
-0.0134541***
0.0070035*
0.0265304***
-0.0050393***
(0.0009316)
(0.0036661)
(0.0038312)
(0.0004464)
0.0007751***
-0.0097443***
0.0100142***
0.0002055**
(0.0002688)
(0.0014618)
(0.0013376)
(0.0000932)
0.0000198
2.36E-04
-0.000277
-3.18E-06
(0.0000652)
(0.0004889)
(0.0004449)
(0.000045)
-3.97E-06
-6.33E-06
0.0000248
-5.20E-06
(0.0000181)
(0.0001236)
(0.000108)
(0.0000121)
TAN
0.0767522***
0.0556782***
0.1814536***
-0.0179037***
(0.004602)
(0.0090614)
(0.0108777)
(0.0017739)
NDTS
-0.0048856
-0.051663***
0.0430518***
-0.0020338
(0.0035283)
(0.0071862)
(0.0071556)
(0.0013548)
-1.57E-07
0.00000133**
-2.26E-07
-0.000000174**
(0.000000144)
(0.000000591)
(0.000000715)
(8.12e-08)
-0.0001292***
-0.0013166***
0.0008979***
-0.0002097***
(0.000026)
(0.0001072)
(0.0001279)
(0.000015)
-0.0000881***
-0.0001354
-0.0008135***
0.0000205
(0.00002)
(0.0001167)
(0.000106)
(0.0000127)
0.0644435***
0.5561194***
-0.4685624***
0.0579193***
(0.0101044)
(0.0347116)
(0.0383869)
(0.0094322)
INDUSTRY Dummies
Yes
Yes
Yes
Yes
YEAR Dummies
Yes
Yes
Yes
Yes
10096
10096
10096
10096
PROFIT
GRWOPP
DIV
RISK
AGE
CONS
N
The standard errors are shown in parentheses below coefficient estimates.
***, **, and * indicate significance at 1%, 5%, and 10% respectively.
31
Table 6: Number of observations and percentages per industry for each sample
This table provides a breakdown by industry of the firm-year observations for all the
samples. Panel A shows the full and matched subsidiary effect samples while Panel B shows
the full and matched location/distance effect samples.
Panel A:
Full sample - SUB effect
Matched sample - SUB effect
Number
% of total
Number
% of total
AGR
161
1%
62
1%
MIN
60
1%
18
0%
MAN
2105
18%
890
16%
BUIL
1841
16%
962
17%
TRA
3134
27%
1686
30%
SER
4461
38%
2052
36%
Total
11762
100%
5670
100%
Panel B:
Full sample - LOC effect
Matched sample - LOC effect
Number
% of total
Number
% of total
AGR
62
1%
12
0%
MIN
99
1%
40
1%
MAN
3076
30%
1122
29%
BUIL
908
9%
96
2%
TRA
1924
19%
720
18%
SER
4027
40%
1906
49%
Total
10096
100%
3896
100%
32
Table 7: Descriptive statistics for UK domestic full sample and UK domestic matched sub effect sample
This table shows the descriptive statistics for the UK domestic firms full and matched sample. The variables shown in
the first column are described in Table 1.
Variable
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
SIZE
PROFIT
GRWOPP
TAN
NDTS
DIV
RISK
AGE
Nbr of
observations
Sample
Mean
Median
Std. Dev.
Minimum
Maximum
6283
UK DOM-FULL SAMPLE
0.2606941
0.17372
0.2778187
0
0.997273
2835
UK DOM-MATCHED
SAMPLE
0.2516051
0.166571
0.2721387
0
0.965991
6283
UK DOM-FULL SAMPLE
0.17041
0.101328
0.1981579
0
0.997273
2835
UK DOM-MATCHED
SAMPLE
0.1670929
0.101133
0.1927001
0
0.968708
6283
UK DOM-FULL SAMPLE
-0.0045842
0
0.3191098
-0.9739591
0.997273
2835
UK DOM-MATCHED
SAMPLE
-0.0012292
0
0.3070983
-0.968235
0.965168
6283
UK DOM-FULL SAMPLE
-0.044058
0
0.29379233
-0.975764
0.997273
2835
UK DOM-MATCHED
SAMPLE
-0.029142
0
0.28663467
-0.965991
0.955036
6283
UK DOM-FULL SAMPLE
9.476891
9.509037
1.423838
1.178655
16.04907
2835
UK DOM-MATCHED
SAMPLE
9.396508
9.423272
1.337671
3.744148
15.93405
6283
UK DOM-FULL SAMPLE
1989.957
549.398
17400
-218252
689000
2835
UK DOM-MATCHED
SAMPLE
1934.543
492
18208.65
-24289
597000
6283
UK DOM-FULL SAMPLE
5.36974
0.057338
180.3983
-0.994905
11647.46
2835
UK DOM-MATCHED
SAMPLE
2.531987
0.057562
71.21904
-0.974168
3667
6283
UK DOM-FULL SAMPLE
0.2616953
0.167259
0.2720402
0
1.105989
2835
UK DOM-MATCHED
SAMPLE
0.2676915
0.172507
0.2731954
0
0.998159
6283
UK DOM-FULL SAMPLE
0.0307753
0.020767
0.0368714
-0.072063
1.069625
2835
UK DOM-MATCHED
SAMPLE
0.0317763
0.021953
0.033974
-0.035212
0.274381
6283
UK DOM-FULL SAMPLE
275.7298
0
2108.949
0
96000
2835
UK DOM-MATCHED
SAMPLE
247.1018
0
1971.121
0
59947
6283
UK DOM-FULL SAMPLE
85.08356
91
14.62327
6
96
2835
UK DOM-MATCHED
SAMPLE
85.28959
91
14.34364
6
96
6283
UK DOM-FULL SAMPLE
21.0347
16
17.98707
0
131
2835
UK DOM-MATCHED
SAMPLE
20.64268
16
17.4749
1
102
33
Table 8: Descriptive statistics for UK SUB full sample and UK SUB sub effect and loc effect matched samples
This table shows the descriptive statistics for the UK subsidiary firms full and matched samples. The variables shown
in the first column are described in Table 1. For the matched samples, we have two matched samples, one for the
subsidiary effect and one for the location effect.
Variable
TOTEXTDEBT
Nbr of
observations
Sample
Mean
Median
Std. Dev.
Minimum
Maximum
5479
UK SUB-FULL SAMPLE
0.0707192
0
0.1512614
0
0.985577
0.0723553
0
0.1477864
0
0.919844
0.0636169
0
0.1308468
0
0.859084
0.288567
0.20348
0.2819823
0
1
0.2851903
0.19479
0.2824898
0
1
0.2769549
0.203659
0.2651646
0
1
0.2100224
0.150266
0.3679089
-1
1
0.2147397
0.153169
0.364896
-1
1
0.2128411
0.157299
0.333317
-0.9833447
1
0.0267269
0
0.1468935
-0.89387
0.985577
0.0319951
0
0.1458874
-0.81277
0.899022
0.0384378
0
0.1267205
-0.7803
0.859084
9.190595
9.189219
1.372842
0.916691
14.19755
9.33217
9.373649
1.372212
1.622946
13.34886
9.151353
9.1342
1.193377
4.465908
12.67079
0.0773198
0.048965
0.3126372
-12.2758
5.064006
0.0713896
0.044817
0.3954078
-12.2758
5.064006
0.061876
0.054305
0.4181025
-12.2758
2.646747
0.4418168
0.053524
7.836428
-0.99241
354.1754
0.333501
0.056425
3.44529
-0.99241
119.2727
0.1750479
0.055493
1.008804
-0.96561
29
0.1485836
0.034279
0.2338032
0
0.997268
0.1423613
0.027645
0.2345528
0
0.997268
0.1075531
0.024055
0.1791987
0
0.966138
0.0522035
0.019954
0.3790334
0
16
0.0398692
0.017618
0.278574
0
8.418283
0.0387614
0.020302
0.2105106
0
7.254406
538.4337
0
2781.445
0
107600
556.5787
0
2868.93
0
107600
367.9803
0
1198.881
0
14600
83.28308
90
15.72818
1
96
83.39224
90
15.54157
9
96
83.58008
90
15.95896
1
96
21.1973
16
18.66869
1
144
21.25926
16
18.45617
1
144
19.93994
16
16.28171
1
111
2835
1948
STDEBT
5479
2835
1948
DIFFSTLTDEBT
5479
2835
1948
DIFFSTLTEXTDEBT
5479
2835
1948
SIZE
5479
2835
1948
PROFIT
5479
2835
1948
GRWOPP
5479
2835
1948
TAN
5479
2835
1948
NDTS
5479
2835
1948
DIV
5479
2835
1948
RISK
5479
2835
1948
AGE
5479
2835
1948
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
UK SUB-FULL SAMPLE
UK SUB-MATCHED
SUB
UK SUB-MATCHED
LOC
34
Table 9: Descriptive statistics for US Sub full sample and US SUB matched loc effect sample
This table shows the descriptive statistics for the US Subsidiary firms full and matched sample. The variables shown in the
first column are described in Table 1.
Variable
TOTEXTDEBT
Nbr of
observations
4617
1948
STDEBT
4617
1948
DIFFSTLTDEBT
4617
1948
DIFFSTLTEXTDEBT
4617
1948
SIZE
4617
1948
PROFIT
4617
1948
GRWOPP
4617
1948
TAN
4617
1948
NDTS
4617
1948
DIV
4617
1948
RISK
4617
1948
AGE
4617
1948
Sample
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
US SUB-FULL
SAMPLE
US SUB-MATCHED
SAMPLE
Mean
Median
Std. Dev.
Minimum
Maximum
0.036773
0
0.1119782
0
0.8453054
0.036882
0
0.109519
0
0.8453054
0.3014235
0.237372
0.2742961
0
1
0.2995427
0.235438
0.274615
0
1
0.2392496
0.194734
0.3488389
-1
1
0.2389035
0.191196
0.3475074
-1
1
0.0152688
0
0.1081281
-0.83716
0.8453054
0.011917
0
0.1074802
-0.73187
0.8453054
9.232716
9.185181
1.24416
0.693147
13.69335
9.194486
9.166307
1.173774
5.135263
13.13094
0.2230337
0.061228
7.907892
-6.7619
529
0.1058014
0.060526
1.453929
-3.17235
62.23756
0.7706488
0.052144
28.86967
-39.4244
1910
0.5365253
0.06189
9.70765
-0.96448
347.25
0.0932316
0.024466
0.1486944
0
1
0.0883659
0.01969
0.1424409
0
0.9807159
0.0311415
0.019121
0.0417209
0
0.8811172
0.0298087
0.018448
0.0391711
0
0.4995523
548.6533
0
3588.269
0
135500
380.0403
0
2158.946
0
49000
82.5822
90
15.89905
1
96
82.59651
90
15.904
1
96
17.75417
14
14.86483
1
94
17.48614
13
15.82536
1
94
35
Table 10: Subsidiary Effect - Matched Sample
This table provides the estimates of an unbalanced panel data regression model for the 5670 firm-year observations using
UK domestic and UK subsidiaries data. The variables used as column headings are the dependent variables used while the
variables shown in the first column are our independent variables. All the variables are defined in Table 1. CONS is our
constant and N the total number of observations. All our standard errors are corrected for heteroscedasticity. The standard
errors are shown in parentheses below coefficient estimates. ***, **, and * indicate significance at 1%, 5%, and 10%
respectively.
Hypothesis 1
SUB
SIZE
Hypothesis 2
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
-0.1612753***
0.1134978***
0.162713***
0.0142151***
(0.0021245)
(0.0026413)
(0.0013348)
(0.0007091)
-0.0004644
-0.0250453***
0.0245876***
-0.0026989***
(0.000512)
(0.0010762)
(0.0007465)
(0.0002773)
1.23E-08
0.000000254***
2.28E-08
-2.11E-07
(0.000000234)
(0.0000000592)
(0.000000251)
(1.74e-07)
0.0001578***
-0.0000433***
0.0002203***
-0.0002048***
(0.00000769)
(0.00000501)
(0.00000551)
(0.0000126)
TAN
0.2988889***
0.0578848***
0.3359226***
-0.2573821***
(0.0046081)
(0.0046991)
(0.0029755)
(0.0011569)
NDTS
-0.0218995***
-0.0188957
0.0165019
0.0148495***
(0.0070814)
(0.0149897)
(0.0150069)
(0.0051054)
5.22E-07
-0.000000895***
1.19E-06
-1.92e-06***
(0.000000387)
(0.00000027)
(0.000000881)
(4.07e-07)
-0.0006948***
-0.0009441***
0.0007195***
-0.0009049***
(0.0000319)
(0.0000763)
(0.0000612)
(0.0000244)
-0.0002065***
-0.000288***
-0.0005369***
0.0006333***
(0.0000335)
(0.0000615)
(0.0000451)
(0.0000175)
0.2841637***
0.5682876***
-0.4922236***
0.2139369***
(0.0169162)
(0.0169084)
(0.0294243)
(0.0134199)
INDUSTRY Dummies
Yes
Yes
Yes
Yes
YEAR Dummies
Yes
Yes
Yes
Yes
N
5670
5670
5670
5670
PROFIT
GRWOPP
DIV
RISK
AGE
CONS
The standard errors are shown in parentheses below coefficient estimates.
***, **, and * indicate significance at 1%, 5%, and 10% respectively.
36
Table 11: Location Effect - Matched Sample
This table provides the estimates of an unbalanced panel data regression model for the 3896 firm-year observations
using UK and US subsidiaries data. The variables used as column headings are the dependent variables used while
the variables shown in the first column are our independent variables. All the variables are defined in Table 1. CONS
is our constant and N the total number of observations. All our standard errors are corrected for heteroscedasticity.
The standard errors are shown in parentheses below coefficient estimates. ***, **, and * indicate significance at 1%,
5%, and 10% respectively.
Hypothesis 1a
SUB
SIZE
Hypothesis 2a
TOTEXTDEBT
STDEBT
DIFFSTLTDEBT
DIFFSTLTEXTDEBT
-0.0162068***
0.0156527***
0.0398184***
-0.0126835***
(0.0011876)
(0.003793)
(0.0047595)
(0.0007218)
0.0019359***
-0.0025588
0.0048538*
0.0010598***
(0.000341)
(0.0020478)
(0.0024917)
(0.0001336)
0.0004225
-2.38E-03
0.0028532
-2.90e-06
(0.0004051)
(0.002046)
(0.0018906)
(0.000268)
-0.0000741
-0.0002922
0.000215
-0.0000112
(0.0001342)
(0.0004037)
(0.0003526)
(0.000071)
0.0985587***
-0.0389831***
0.2931027***
-0.021386***
(0.0061053)
(0.0092152)
(0.0147692)
(0.0017586)
-0.0124003*
-0.0800947***
0.0420602**
0.0007453
(0.0064531)
(0.0154816)
(0.0177601)
(0.0033443)
-0.000000647**
-0.00000551***
0.00000308**
-9.17E-08
(0.000000304)
(0.0000014)
(0.00000135)
(8.83e-08)
RISK
-0.0002322***
-0.0006737***
0.0007114***
-0.0001322***
(0.000034)
(0.0001593)
(0.0002043)
(0.0000114)
AGE
-0.0000344*
-0.0007261***
-0.000203
0.0000129
(0.0000191)
(0.0001399)
(0.0001847)
(8.06e-06)
0.0091715*
0.4352942***
-0.3555914***
0.0075095**
(0.0048485)
(0.0922392)
(0.0932863)
(0.0032545)
Yes
Yes
Yes
Yes
PROFIT
GRWOPP
TAN
NDTS
DIV
CONS
INDUSTRY Dummies
YEAR Dummies
Yes
Yes
Yes
Yes
N
3896
3896
3896
3896
The standard errors are shown in parentheses below coefficient estimates.
***, **, and * indicate significance at 1%, 5%, and 10% respectively.
37
1
Subsidiary effect or hypothesis 1 are used interchangeably throughout the paper.
2
Location/distance effect and hypothesis 2 are used interchangeably throughout the paper.
US BEA data is currently only available up to year 2009.
4
The figures for UK domestic firms are not exactly zero as we selected these firms on the basis of being standalone firms thus not having access to group loans. However, director loans are considered internal financing and
explain why it is not exactly equal to zero for UK domestic firms.
3
5
Although we do not report these results, in line with previous literature, we confirm that both our UK and US
subsidiary firms have higher amounts of internal debt and as a result report lower absolute amounts of
external debt.
6
7
This has now also been introduced since May 2011 in IFRS 10 on consolidated financial statements.
ECR stands for Euromoney country risk.