Vol. 51, No. 5, Oct. 2016, pp. 1611–1636
COPYRIGHT 2016, MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON, SEATTLE, WA 98195
doi:10.1017/S0022109016000673
The Strategic Behavior of Firms with Debt
Jérôme Reboul and Anna Toldrà-Simats*
Abstract
We empirically study the strategic behavior of levered firms in competitive and noncompetitive environments. We find that regulation induces firms to increase leverage, and this
reduces their ability to compete when deregulation occurs. Large and small levered firms
adopt different strategies upon deregulation. Whereas more levered small firms charge
higher prices to increase margins at the expense of market shares, highly levered large
firms prey on their rivals by increasing output and reducing prices to increase their market shares. The difference in their behavior is due to differences in their probability of
bankruptcy and their financing constraints.
I.
Introduction
This paper examines the relationship between firm leverage and product market competition and emphasizes the distinct strategic behavior of large and small
firms.
Several authors provide competing theories for the strategic effect of leverage
on competition. Brander and Lewis (1986) argue that highly levered firms can
decide to compete more aggressively in the product market by increasing their
output. This result is due to the option-like payoff of equityholders. Chevalier
and Scharfstein (1996) and Dasgupta and Titman (1998) postulate instead that
more levered firms have incentives to increase prices in order to secure short-term
profits. This result is due to the higher discounting associated with the higher
probability of bankruptcy of levered firms. Although empirical papers have found
support for each theory separately,1 these papers have provided little guidance
*Reboul, [email protected], Savings and Financial Markets Unit, French Treasury;
Toldrà-Simats (corresponding author), [email protected], Universidad Carlos III de Madrid. We
thank Claude Crampes, Bruno Jullien, Antoine Loeper, Paul Malatesta (the editor), Juan-Ignacio Peña,
Gordon Phillips (the referee), Pablo Ruiz-Verdú, Paola Sapienza, Daniel Wolfenzon, Luigi Zingales,
and participants at the 2014 Spanish Association of Finance (AEFIN) Conference for helpful comments. Toldrà-Simats thanks the Spanish Ministerio de Ciencia y Tecnologia for the financial support received (Grant ECO2012-36559) and Fundación Ramon Areces for the research funding (Grant
2015/00442/001).
1
See review by Parsons and Titman (2008).
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https://doi.org/10.1017/S0022109016000673
JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS
Journal of Financial and Quantitative Analysis
about the conditions under which one theory is more relevant than the other.2
We explore these issues by examining electricity generation firms pre- and postderegulation in a large sample of European countries.
Our main contribution is to show that the characteristics of a given firm
determine which of the competing theories best describes its behavior. We find
that leverage leads large producers to invest more and increase their output. This
strategic behavior reduces their profit margins and increases their market shares,
consistent with Brander and Lewis (1986). In contrast, small firms with higher
leverage charge higher prices. This behavior drives their margins up and lowers
their future market shares, consistent with Chevalier and Scharfstein (1996) and
Dasgupta and Titman (1998).
We try to uncover the fundamental conditions of large and small firms that
cause them to behave differently. Our results show that large competitors are
less financially constrained than small ones. The lower financial constraints allow them to raise more external funds to expand. This explains why the logic of
the Brander and Lewis (1986) model better describes the incentives of large firms.
Conversely, we find that more levered small firms are closer to financial distress
than large firms. The higher probability of distress explains why the strategic behavior highlighted in the Chevalier and Scharfstein (1996) and Dasgupta and Titman (1998) models better describes the incentives of small firms.
Our second contribution is that we address endogeneity in the leverage–
competition relationship by exploiting the fact that our data set covers several
years before the deregulation of the European electricity industry and several
years after. Deregulation provides an exogenous shock to competition. According to the trade-off theory of debt, firms respond to shocks in the product market
by adjusting their capital structures in order to balance the costs and benefits of
borrowing. We find support for this theory because, in our sample, deregulation
leads firms to reduce their debt ratios. However, if the strategic theory of debt is
true, firms should not only change their capital structures in response to deregulation merely to bring leverage back to its optimal level (see Ovtchinnikov (2010)),
but should also consider refinancing as a way to affect their competitive position
and survival in the newly deregulated market. We use instrumental variables as a
source of exogenous variation in debt levels to identify the causal effect of debt
on product market outcomes. Moreover, by comparing the differential effect of
leverage on competition before and after deregulation, we can isolate the strategic
effect of debt.
A methodological problem in this literature is that the effects of a firm’s own
debt and those of rivals’ debt are sometimes not separately identified empirically.
Such empirical models are inconsistent with the theory, which predicts that these
two variables differently affect the ability of a firm to compete in the product
market.3 Failing to include the behavior of competitor firms in the empirical analysis may lead to an overestimation of the true effect of firm leverage on firms’
2
An exception is the paper by Phillips (1995), which provides evidence of differential behavior of
firms at the industry level.
3
A more levered firm is more likely to underinvest (Myers (1977)) or to lose customers (Titman
(1984)). A firm facing low-levered competitors (i.e., deep pockets) is more likely to suffer from predatory behavior (i.e., in the form of price cuts, aggressive marketing campaigns, or the construction
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https://doi.org/10.1017/S0022109016000673
1612
1613
competitive outcome and induce an incorrect interpretation regarding the mechanism behind this relationship.4 In our setting, if we assume that each country is a
market, we are able to account for market-level time-varying changes due to the
aggregate effect of rival firms’ leverage by including an interacted country-year
fixed effect.5
Finally, our data set also allows us to analyze the role of firm debt in a regulated environment and the unintended consequences of regulation. We find that
before deregulation, firms have incentives to take on excessive debt to increase the
price set by the regulator. The reason is that the regulated price takes into account
the cost of debt but not the cost of equity. Interestingly, leverage has no positive
effect on market shares before deregulation. This suggests that the high level of
firms’ leverage during regulation is not a strategy to be more competitive but simply a way to manipulate the regulated price. The problem is that because it takes
time for firms to adjust their capital structures, the excessive leverage induced
by regulation reduces firms’ ability to compete once deregulation takes place and
increases their likelihood of exit from the market.
This paper is part of the literature that relates capital structure and product
market competition. The pioneer empirical papers by Phillips (1995), Chevalier
(1995), Kovenock and Phillips (1997), and Zingales (1998) analyze the effect of
leverage on competition and exit in several industries where firms sharply increased leverage. The first paper is the closest to ours. In that paper, Phillips emphasizes the role of capacity constraints and the role of costly external financing in
the differential behavior of firms in four different industries. Our findings follow
a similar logic, but whereas Phillips (1995) emphasizes differences across industries, our paper emphasizes differences between large and small firms within the
same industry. Moreover, by focusing on firm-level behavior, we are able to abstract from unobserved industry-specific effects. We also study firm behavior before and after deregulation to see how firms’ strategies change after an exogenous
shock. The paper by Chevalier (1995) focuses on pricing strategies and exit and
entry in the supermarket industry after the supermarket chains undertook leveraged buyouts (LBOs). The paper by Kovenock and Phillips (1997) also focuses
on exit and expansion but at the plant level. Zingales (1998) studies the effect of
leverage on the survival of firms in the trucking industry after deregulation. He
finds that highly levered carriers are less likely to survive deregulation. Because
his major concern is that capital structure could reflect unobserved heterogeneity
of more efficient plants), as postulated by Bolton and Scharfstein (1990) and Fudenberg and Tirole
(1986).
4
For example, the negative effect of leverage on prices found in some papers (e.g., Chevalier
(1995), Zingales (1998)) can be interpreted as a strategy to mitigate losses in the customer base by
reducing prices or as a forced reaction to the predatory behavior of competitors. Zingales (1998) finds
an indirect way to overcome this problem. He argues that because the price decline is almost exclusively attributable to a segment of the trucking industry in which a great proportion of value results
from customer service, this is evidence that leveraged carriers in this segment discount their services
to compensate consumers for the risk associated with the probability of default. He also explains that
because entry into this trucking segment is relatively easy, and because predation is most effective in
situations in which some capital is destroyed, it is difficult to imagine that predatory pricing would be
particularly effective in this segment.
5
This method is advocated by Gormley and Matsa (2014).
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https://doi.org/10.1017/S0022109016000673
Reboul and Toldrà-Simats
Journal of Financial and Quantitative Analysis
in efficiency, he controls for this particular endogeneity problem, but does not try
to draw conclusions on the effect of capital structure on market outcomes or determine the effect of rival firms. A related paper by Ovtchinnikov (2010) tests the
trade-off theory of debt but not the strategic theory of debt. Finally, Frésard (2010)
also studies the effect of financing decisions on market shares, but he focuses on
cash instead of leverage.
This paper is organized as follows: In Section II, we describe the deregulation process of the electricity industry in Europe. Section III describes our data. In
Section IV, we describe how deregulation changes the competitive environment
of firms. In Section V, we study the effect of capital structure on competition.
In Sections VI and VII, we study how large and small firms react to deregulation. In Section VIII, we study the effect of deregulation on leverage. Section IX
concludes.
II.
Electricity Deregulation in Europe
In Europe, the deregulation of electricity markets started with the establishment of the England and Wales market pool in 1990. The first European Union
(EU) directive for the deregulation of electricity in continental Europe was established in 1996 with Directive 96/92EC, and further amendments were made in
2003 with Directive 2003/54/EC. This process officially started the liberalization
of the electricity industry in Europe.
This industry was traditionally organized with the vertical integration of all
sections in the electricity chain: generation, transmission, distribution, and supply. Generation is the production of electricity. Transmission takes place through
the electricity highways from the producer to the designated region. Distribution
is carried out by the regional and local electricity networks. Supply is the delivery of electricity to the end customers. In the electricity chain, the network
units, transmission and distribution, are noncompetitive and constitute natural monopolies. The objective of the EU directives is to introduce competition at the
commercial-unit level (i.e., generation and supply). According to these directives,
the key element of deregulation is to guarantee that market participants compete
with each other for market shares in both the wholesale and retail markets by
enjoying nondiscriminatory access to the still monopolized transmission and distribution units.6
Even though all member states had to deregulate their markets by the specified deadlines, different countries transposed the electricity directive at different
dates.
Table 1 displays the transposition dates for the 13 countries in our sample.
These dates coincide with the European Commission’s statement of deregulation
in its 2005 benchmark report.7
6
The text of the directives can be found at http://aei.pitt.edu/38941/1/SEC (2005) 1448.pdf
Report on progress in creating the internal gas and electricity market (COM/2005/0568), Technical Annex, European Commission (2005).
7
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TABLE 1
Electricity Transposition Dates by Country
Table 1 reports the name of the deregulation law and the date on which it was adopted by each country in our sample.
Source: European Commission benchmark report (2005).
Country
Law and Transposition Date
Austria
Belgium
Denmark
Finland
France
Germany
Greece
Italy
Luxembourg
Portugal
Spain
Sweden
United Kingdom
Law of Electricity Supply—1998
Law for the Organization of the Electricity Market—Apr. 29, 1999
Amendment to Danish Supply Act—1998
Electricity Market Act—1995
Law No. 2000-108—2000
Act on the Supply of Electricity and Gas—1999
Electricity Law—2000
Law 239/2004—2004
Law of July 24, 2000 on the Organization of the Electricity Market—2000
Infringement—Aug. 2006
Electricity Power Act—Nov. 1997; amendments—1998
Law for the Supply of Electricity—1999
Electricity Act—1989
III.
Data
We built our main data set from Datastream. We selected all listed firms for
which the main activity was electricity generation in the EU countries for the
period between 1990 and 2006. Our data set includes firms in 13 countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Luxembourg,
Spain, Sweden, Portugal, and the United Kingdom. We classify each country as a
market, and we assume that each firm has the majority of its business activity in
the country/market where it is listed. Firms from new member countries are not
included in our sample because they were not subject to the deregulation directives before entering the EU. Firms in the Netherlands and Ireland were not listed
during our sample period. There are 97 companies in our sample. In terms of our
main variable, which is leverage, our sample has 40 to 56 observations per year.
The number of total usable firm-year observations is 947.
The unbalanced nature of the panel is due to acquisitions or liquidations of
existing firms as well as entry. We know when a firm exits because it disappears
from the Datastream database and is recorded as dead. Most of the dead firms in
our sample are acquired by other firms, and only a minority of them are liquidated.
We consider that a firm enters the industry when it appears for the first time in
Datastream during our sample period. There are 65 surviving firms in our sample;
that is, 65 of the 97 (67%) firms are present in our data set during the whole period.
There are 13 firms that exit the sample and 19 firms that are entrants. Most exits
occur around the deregulation year, and all entries occur after deregulation. When
we take into account these movements in our analysis, the number of total usable
firm-year observations in our sample is 755.
Information at the firm level includes most of the firms’ balance sheet variables and their key accounts reports. Information at the European level comes
from Eurostat.
IV.
The Effect of Deregulation: Descriptive Statistics
We begin our analysis with simple summary statistics of the main
variables used in the paper. The definitions of these variables can be found
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Reboul and Toldrà-Simats
Journal of Financial and Quantitative Analysis
in the Appendix. Table 2 reports a comparison of the pre- and postderegulation averages of our main variables using only the survivor firms in our
sample.
Industrial prices and market shares decreased significantly after deregulation,
suggesting that deregulation introduced a higher degree of competition among
firms. The greater competition together with the numerous exits of firms postderegulation is consistent with an increase in firms’ probability of distress after deregulation. The greater likelihood of distress is likely to explain the observed significant decrease in firms’ debt. Despite the decrease in prices, profit
margins increased on average, which is due to a larger decrease in firms’ costs
after deregulation.8 Lower costs together with the fact that firms’ growth opportunities, profitability, intangibles, and cash significantly increased, whereas fixed
assets and inventories decreased, suggest an improvement in the efficiency of
survivor firms.9
It is clear from this preliminary analysis that deregulation brought significant
changes to firms’ competitive environment. As a result, the way in which leverage affected firms’ competitive behavior must have changed after the deregulation
shock. In Sections V, VI, and VII, we find evidence of such changes, which provides support for the strategic theory of debt.
Moreover, due to the greater likelihood of distress in the post-deregulation
period, firms’ costs of holding debt presumably increased, leaving firms with a
level of leverage above the optimal one when deregulation took place. In Section VIII, we provide evidence that firms reacted to deregulation by decreasing
their leverage.
TABLE 2
Effect of Deregulation on Firms’ Operating Environment
Table 2 shows summary statistics for the main variables in our analysis before and after deregulation. All variables are
defined in the Appendix. All computations in this table include only survivor firms in the sample (i.e., entrant and exiting
firms are excluded).
Variable
Mean Before (Std. Dev.)
Mean After (Std. Dev.)
Difference t -Stat. No. of Obs.
TOTAL_DEBT
TOTAL_ASSETS
FIRM_LEVERAGE
SIZE
GROWTH_OPPORTUNITIES
PROFITABILITY
INTANGIBLES
CASH
ROIC
FIXED_ASSETS
INVENTORIES
PROFIT_MARGINS
MARKET_SHARES
INVESTMENT
PRICES
2,863,270 (330,827)
9,303,953 (1,041,046)
0.21 (0.01)
14.22 (0.12)
0.80 (0.04)
0.03 (0.003)
0.032 (0.003)
0.079 (0.006)
7.64 (0.84)
0.54 (0.01)
0.05 (0.005)
2.12 (0.06)
0.29 (0.01)
−3.10 (0.08)
0.07 (0.006)
2,357,590 (274,531)
8,940,064 (1,090,618)
0.22 (0.009)
14.4 (0.09)
0.92 (0.02)
0.04 (0.006)
0.04 (0.003)
0.09 (0.006)
8.56 (1.09)
0.52 (0.01)
0.03 (0.002)
2.31 (0.04)
0.23 (0.01)
−3.11 (0.06)
0.06 (0.0005)
−505,679
−1.18
−363,888
−0.24
0.0098
0.72
0.202
1.31
0.12
2.52
0.012
1.61
0.011
2.27
0.011
1.28
0.91
0.65
−0.02
−1.15
−0.02
−4.01
0.18
2.42
−0.054
−2.62
−0.011
−0.11
−0.01
−13.4
755
755
755
755
565
754
753
754
714
755
753
628
686
603
755
8
The costs of goods sold significantly decreased (t-statistic = 1.4), the number of employees significantly decreased (t-statistic = 1.38), and the selling general and administrative expenses also decreased significantly (t-statistic = 2.5).
9
These results are consistent with previous empirical findings, such as those of Frank and Goyal
(2003) and Ovtchinnikov (2010).
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V.
1617
The Effect of Capital Structure Changes on Competition
In this section, we test the strategic theory of debt by investigating the effect
of leverage on the competitive outcome of firms before and after markets were
liberalized.
A.
Empirical Strategy
We start the analysis by running an ordinary least squares (OLS) regression
where the dependent variable is a measure of competition. We use two measures
of competition: firms’ profit margins and their market shares. By definition, the
strategic theory of debt suggests that leverage has a different impact on firm behavior depending on whether markets are competitive or not. Hence, our set of
explanatory variables includes two main variables of interest: firms’ leverage and
firms’ leverage interacted with a deregulation dummy. The first variable captures
the effect of firms’ leverage decisions on their margins and market shares before
deregulation; the second one captures the impact of deregulation on this effect.
We address the potential endogeneity problem in the leverage–competition
relationship using an instrumental variables approach where we instrument postderegulation firm leverage.10 Firms have incentives to adjust debt for reasons that
are not directly related to competition. Any variable that captures these incentives
is potentially a good instrument provided that it can reasonably be assumed exogenous with respect to the competitive outcome. We use two types of tangible assets
as our instruments for post-deregulation firm leverage: fixed assets, defined as the
ratio of firms’ property, plant, and equipment (PPE) to total assets; and inventories, defined as the ratio of inventories to total assets.11 In electricity-generating
companies, PPE consists of power plants and machinery, whereas inventories are
the spare parts, servicing equipment, and raw materials used in the production
process. We can justify these instruments as follows: First, tangible assets are
commonly used as collateral for leverage; second, the correlations of these instruments with our two main competition measures are low, which suggests that these
two instruments are likely to satisfy the exclusion restriction.12
The following equation represents our first-stage regression:
(1)
LEVitj
=
j
j
Fi,t−1
α + (1dereg j,t−1 × Fi,t−1
)αd
j
j
+ Ii,t−1
α 0 + (1dereg j,t−1 × Ii,t−1
)αd0 + X itj γ1 + δi + δtj + u itj ,
10
Post-deregulation firm leverage is clearly the potential endogenous variable in our analysis and
the one for which we want to determine the causal effect. Thus, we conduct our analysis by instrumenting this variable. The endogeneity of pre-deregulation firm leverage is less clear due to the nature
of a regulated environment, where firms have less room to strategically affect competition with leverage. Moreover, as recommended by Angrist and Pischke (2009), it is better to start an instrumental
variables analysis by instrumenting only one endogenous variable. Nevertheless, in an unreported set
of regressions, we run the tests instrumenting both leverage and its interaction with deregulation. The
results are qualitatively similar to the results reported in this paper.
11
Tangible assets have been used as instruments for leverage previously in the literature by
Aivazian, Ge, and Qiu (2005) and Frésard (2010). Moreover, a recent paper by Oztekin (2015) shows
that tangible assets are a reliable determinant of leverage.
12
The correlations between profit margins and fixed assets and inventories are 0.01 and 0.02, respectively. The correlations between market shares and fixed assets and inventories are 0.03 and 0.003,
respectively.
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Reboul and Toldrà-Simats
Journal of Financial and Quantitative Analysis
where LEVitj is the endogenous variable and corresponds to the post-deregulation
ratio of debt to book value of assets of firm i in country j at time t. Our instruments Fitj and Iitj correspond to firms’ fixed assets and inventories, respectively.
They are lagged 1 period to avoid simultaneity. Each of these two instruments
enters our first-stage regression both directly and interacted with the deregulation dummy 1dereg jt , which equals 1 for firms in countries that underwent deregulation after deregulation occurred, and 0 otherwise. The coefficient α captures
the effect of fixed assets on pre-deregulation leverage decisions, and the coefficient αd captures how the increased competition brought by deregulation affects
this relationship. The coefficients α 0 and αd0 capture the corresponding effects for
inventories.
We use the pre- and post-deregulation versions of our instruments because
we expect them to have a different impact on post-deregulation leverage. In a
liberalized market, firms are more likely to go bankrupt. Because creditors want
to be protected in the event of firm default, more tangible assets should allow firms
to undertake higher debt levels because buildings, equipment, and inventories can
be used as collateral. Thus, we expect tangible assets to be positively correlated
with leverage after deregulation. The effect of tangible assets before deregulation
on leverage after deregulation is less clear. On the one hand, firms that had more
tangible assets before deregulation might have borrowed more to finance those
assets, and they may still have more assets and be more levered when deregulation
takes place. In this case, we expect a positive relationship. On the other hand, it
may be the case that firms with higher tangible assets before deregulation are
firms with lower investment needs, and they need to borrow less once the industry
deregulates. In this case, we expect a negative relationship.
The set of included instruments is denoted by X itj , and it corresponds to
the covariates in our 2-stage least squares regression. The first-stage regression,
similar to the following regression, includes firm fixed effects (δi ) and an interacted country-year fixed effect (δtj ), as well as heteroskedasticity-robust standard
errors (u itj ).
We run the following instrumental variables regression using 2-stage least
squares:
(2)
j
Yi,t+1
=
LEVitj β1 + (1dereg \
× LEVitj )β2 + X itj γ2 + δi + δtj + εitj ,
j,t
j
where Yi,t+1
is a measure of the competition of firm i in country j at time t + 1.
The first measure is the natural logarithm of firms’ profit margins, defined as the
ratio of operating income to net sales. The second measure is firms’ market shares,
computed as the ratio of the revenues from the sales of firm i in year t to the total
market revenues in year t.13 Our competition measures are lagged 1 period forward to capture the effect of leverage on future margins and market shares. The
13
This measure of market share does not take into account firms that are not in our sample but might
be competitors of our firms. Unfortunately, an overall measure of concentration, such as the Herfindahl
index or the market shares of all the electricity operators, does not exist for our sample period. Eurostat
provides the market shares of the largest operator in each country from 1999 onward; however, this
reduces our number of cases to 13 and the number of years to mainly the post-deregulation period.
Hence, we choose not to use this data.
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1619
coefficient β1 captures the effect of firm leverage on competition before deregulation. The main coefficient of interest is β2 . It captures the effect of firms’ strategic
leverage decisions on their competitive outcomes that are due to deregulation.
Hence, this coefficient captures the strategic theory of debt.
The additional independent variables included in X itj correspond to a set of
firm characteristics, such as growth opportunities (i.e., Tobin’s Q), profitability,
size, intangible assets, cash, and the return on invested capital. These control variables are standard in the literature and are meant to capture additional determinants of firms’ competitive outcomes, such as agency costs, information asymmetries, and efficiency. We include cash as a percentage of total assets because
in a recent paper, Frésard (2010) shows that cash has significant effects on the
competition of firms, and these effects go beyond the effect of leverage. Following the common practice in the field, these variables are lagged once with respect
to the dependent variable to mitigate potential simultaneity problems. We expect
cash and the return on invested capital to have a positive effect on firms’ margins
and market shares, as these two variables can be seen as proxies for the efficiency
of firms. Profitability is usually positively related to margins and market shares;
however, some authors also argue and show empirically that firms might sacrifice
profitability when they engage in a fight for market share.14 In our context, we expect this latter effect to be more likely for large firms. We expect firms with higher
growth opportunities to be less competitive (i.e., to have higher margins and lower
market shares), but a higher Tobin’s Q might also be related to lower margins, for
example, due to underinvestment, which is more likely in small firms. Intangible
assets account for the licenses granted to these firms, customer listings, acquisition value, or strategic alliances. We expect intangible assets to have a positive
effect. Following Frésard (2010), we also include past margins and past market
shares in our margins and market shares regressions, respectively, to capture the
influence of other firm characteristics not included in the regression that may have
driven firms’ competitive performance in the recent years, such as changes in the
distribution or transmission networks or a change in the location of the power
plants.
As explained in the Introduction, the theory suggests that the competitive
outcome of firms is affected by the actions of rivals operating in the same market. We include an interacted country-year fixed effect (δtj ) to capture the strategic
behavior of competitor firms that varies over time. This fixed effect has the advantage of controlling for changes at various levels: It captures any supply or demand
shocks that affect a given market in a given year (e.g., rival firms’ strategies, political changes or economic shocks); it controls for common time effects across
Europe, such as the introduction of the euro; and it controls for country-specific
effects that are constant over time, such as the countries’ legal origins and their
financing systems.
Heterogeneity in firms’ competitiveness is widespread, and likely to be correlated with X itj through the private information of managers regarding their firms’
prospects as well as through a number of omitted variables, such as managerial
quality. We control for firm-level heterogeneity using firm fixed effects (δi ). Our
14
See Hamermesh, Anderson, and Harris (1978).
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Journal of Financial and Quantitative Analysis
sample includes government-owned electricity firms that became privatized and
listed during our sample period (e.g., EDP in Portugal or EDF in France). These
firms may benefit from particular conditions in the credit market. The use of firm
fixed effects in our regressions is also useful to control in a nonparametric way for
all permanent differences these firms could display relative to other firms. In addition to generation, many of the firms in our sample also owned the transmission
or distribution grids before starting to deregulate. The use of firm fixed effects
should also account for differences between these and generation-only firms. Following the suggestion by Bertrand, Duflo, and Mullainathan (2004), our standard
errors (εitj ) are robust.
B.
Regression Results
Table 3 contains various specifications of the empirical model and reports
the regression results.
Panel A of Table 3 reports the effect of leverage on margins, and Panel B
reports its effect on market shares. In both panels, columns 1–4 show the regression results using OLS. Column 5 displays the results of the first-stage regression
of the instrumental variables (IV) strategy.15 Columns 6–8 report the results using
IV.16 In all columns except column 1, we include only the permanent firms in the
sample to make sure that our results are not driven by the entry and exit of firms.
In columns 2 and 6 we include all the survivor firms in the sample. Then, we
separate the sample into two subsamples of small firms (with total assets below
the median of 1.5 million euro) and large firms (with assets above the median).
Columns 3 and 7 report the results for large survivor firms, and columns 4 and 8
report results for small survivor firms.
1.
The Effect of Leverage after Deregulation
Both the OLS and IV coefficients show that large firms with higher leverage
have significantly lower margins due to the deregulation shock: An increase of 1
percentage point in firm leverage decreases large firms’ margins by 1.1% (Table 3,
Panel A, column 7). Regarding market shares, the effect is positive and significant
for large firms (Panel B, columns 3 and 7): An increase of 1 percentage point in
leverage increases large firms’ market shares by 0.3 percentage points (i.e., 2%
with respect to the mean).
There are several possible explanations for the reduced margins of highly
levered large firms. The lower margins could be due to underinvestment in more
efficient technologies or to predatory behavior by rival firms. But these two explanations alone are inconsistent with large firms’ increase in market shares. Instead,
lower margins and greater market shares suggest that deregulation leads large levered firms to adopt more aggressive strategies by increasing output, as predicted
by Brander and Lewis (1986).17 The giant producer EDF is a good example of
15
The F-statistic in the first-stage regression for margins is 24.42, and it is 17.47 in the market
shares regression. Both statistics show that our instruments jointly and significantly explain leverage.
16
A Hausman test rejects the exogeneity of post-deregulation leverage (prob > χ 2 = 0.13), which
provides support for our instrumentation strategy.
17
One could attribute the more aggressive behavior of large firms to the too-big-to-fail argument.
However, this explanation is independent of capital structure and thus cannot explain the impact of
leverage on large firms’ strategies.
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https://doi.org/10.1017/S0022109016000673
1620
TABLE 3
The Effect of Leverage on Competition
Table 3 reports the effect of leverage on competition in the regulated and deregulated environments. In Panel A, the dependent variable is the natural logarithm of firms’ operating profit margins; in Panel B,
this variable corresponds to firms’ market shares. The independent variables of interest are firm leverage and its interaction with the deregulation dummy. The rest of the independent variables are potential
explanatory variables used in the literature. Columns 1–4 report the results of the OLS model. Column 5 shows the results of the first-stage regression of our instrumental variables specification. In columns 6–8,
we report the results of the second-stage regression, where we instrument post-deregulation firm leverage with firms’ tangible assets and their interaction with the deregulation dummy. Column 1 shows the
results for all firms in the sample; the rest of the columns take into account only permanent firms. The regressions in columns 3 and 7 include only a subsample of small firms, and columns 4 and 8 include only
large firms. Firms are divided into small and large firms by the median of total assets (i.e., 1.5 million). Firm fixed effects and an interacted country-year fixed effect are included in all regressions. Standard
errors (shown in parentheses) are robust to heteroskedasticity and autocorrelation. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the
Appendix.
OLS All
OLS Survivors
OLS Large Survivors
OLS Small Survivors
First Stage
IV Survivors
IV Large Survivors
IV Small Survivors
1
2
3
4
5
6
7
8
Panel A. Gross Profit Margins
0.84
683
0.43 (0.04)***
−0.09 (0.37)
−0.74 (0.38)**
0.33 (0.58)
−0.12 (0.06)**
−1.6 (1.0)*
0.012 (0.07)
0.92 (0.27)***
0.001 (0.001)
—
—
—
—
—
—
0.89
415
0.15 (0.05)***
−0.53 (0.3)*
0.88 (0.3)***
−0.39 (0.6)
0.5 (0.14)***
−1.25 (1.27)
−0.09 (0.08)
0.6 (0.6)
0.004 (0.001)***
—
—
—
—
—
—
0.97
238
0.21 (0.07)***
3.4 (0.8)***
−3.1 (0.7)***
−1.25 (1.5)
−0.38 (0.08)***
−1.09 (1.5)
−0.33 (0.12)***
1.8 (0.45)***
0.01 (0.008)
—
—
—
—
—
—
0.93
177
—
—
—
—
—
—
—
—
—
—
—
−0.17 (0.06)***
0.27 (0.04)***
0.03 (0.12)
−0.27 (0.13)**
0.98
415
0.41 (0.04)***
−0.98 (0.6)
−0.02 (0.54)
0.51 (0.61)
−0.03 (0.07)
−1.07 (1.0)
0.04 (0.07)
0.89 (0.28)***
0.001 (0.001)
17.7
2.8 (0.41)
—
—
—
—
0.89
415
0.13 (0.05)**
−1.11 (0.37)***
1.18 (0.34)***
−0.08 (0.6)
0.6 (0.13)***
−1.41 (1.32)
−0.14 (0.08)
0.83 (0.62)
0.004 (0.001)***
24.42
1.46 (0.69)
—
—
—
—
0.96
238
0.2 (0.07)***
3.0 (1.1)***
−3.5 (0.9)***
−2.3 (2.0)
−0.4 (0.13)***
−1.06 (1.6)
−0.31 (0.13)**
1.6 (0.54)***
0.016 (0.01)
11.33
8.02 (0.10)
—
—
—
—
0.92
177
(continued on next page)
1621
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https://doi.org/10.1017/S0022109016000673
R2
No. of obs.
0.36 (0.03)***
−0.59 (0.27)**
−0.06 (0.23)
—
—
—
—
—
—
—
—
—
—
—
—
Reboul and Toldrà-Simats
PROFIT_MARGINS (lag)
LEVERAGE × DEREG
LEVERAGE
INTANGIBLES
GROWTH_OPPORTUNITIES
PROFITABILITY
SIZE
CASH
ROIC
CRAGG_DONALD_STAT
SARGAN_TEST
FIXED_ASSETS
FIXED_ASSETS × DEREG
INVENTORIES
INVENTORIES × DEREG
1622
OLS All
OLS Survivors
OLS Large Survivors
OLS Small Survivors
First Stage
IV Survivors
IV Large Survivors
IV Small Survivors
1
2
3
4
5
6
7
8
Panel B. Market Shares
MARKET_SHARE (lag)
LEVERAGE × DEREG
LEVERAGE
INTANGIBLES
GROWTH_OPPORTUNITIES
PROFITABILITY
SIZE
CASH
ROIC (×100)
CRAGG_DONALD_STAT
SARGAN_TEST
FIXED_ASSETS
FIXED_ASSETS × DEREG
INVENTORIES
INVENTORIES × DEREG
0.56 (0.04)***
0.06 (0.04)
0.03 (0.03)
—
—
—
—
—
—
—
—
—
—
—
—
R2
No. of obs.
0.95
788
0.82 (0.03)***
0.059 (0.03)*
−0.04 (0.03)
0.08 (0.06)
0.0002 (0.005)
−0.04 (0.07)
0.008 (0.007)
−0.00004 (0.02)
−0.008 (0.01)
—
—
—
—
—
—
0.98
488
1.04 (0.07)***
0.12 (0.05)**
−0.0006 (0.05)
0.25 (0.11)**
−0.012 (0.19)
−0.48 (0.19)**
−0.01 (0.01)
0.19 (0.1)*
0.03 (0.01)*
—
—
—
—
—
—
0.98
263
0.68 (0.05)***
−0.17 (0.05)***
0.008 (0.05)
0.28 (0.08)***
−0.003 (0.004)
0.04 (0.07)
−0.04 (0.008)***
0.017 (0.01)
−0.002 (0.02)
—
—
—
—
—
—
0.98
225
—
—
—
—
—
—
—
—
—
—
—
−0.11 (0.05)**
0.17 (0.04)***
0.10 (0.10)
−0.34 (0.11)***
0.98
488
0.83 (0.03)***
0.15 (0.11)
−0.11 (0.08)
0.03 (0.07)
0.003 (0.02)
−0.001 (0.006)
−0.04 (0.08)
0.013 (0.007)*
0.003 (0.1)
9
2.41 (0.49)
—
—
—
—
0.99
488
1.04 (0.07)***
0.30 (0.07)***
−0.09 (0.06)
0.06 (0.12)
−0.012 (0.02)
−0.61 (0.21)***
−0.001 (0.01)
−0.13 (0.11)
0.04 (0.01)**
17.47
4.3 (0.22)
—
—
—
—
0.99
263
0.83 (0.06)***
−0.13 (0.07)**
0.23 (0.06)***
0.29 (0.08)***
−0.01 (0.005)**
−0.03 (0.08)
−0.03 (0.009)***
0.05 (0.02)***
−0.01 (0.02)
19.8
4.0 (0.26)
—
—
—
—
Journal of Financial and Quantitative Analysis
TABLE 3 (continued)
The Effect of Leverage on Competition
0.99
225
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https://doi.org/10.1017/S0022109016000673
1623
this strategy, as the company spent about 19 billion euros to expand its capacity
by acquiring smaller regional firms between 2001 and 2003.18
For small firms, the effect of leverage on margins is positive and significant: An increase of 1 percentage point in leverage increases small firms’ margins by 3% (Table 3, Panel A, columns 4 and 8). And the effect on their market
shares is negative and significant (Panel B, columns 4 and 8): An increase of 1
percentage point in leverage decreases small firms’ market shares by 0.13 percentage points (i.e., 1% with respect to the mean). These two effects suggest that
deregulation leads small firms to behave as predicted by the models of Chevalier and Scharfstein (1996) and Dasgupta and Titman (1998): More levered small
firms undertake strategies that increase their margins (i.e., by reducing output
or charging higher prices). However, this comes at the expense of their future
market shares.19
2.
The Effect of Leverage before Deregulation
Pre-deregulation firm leverage positively and significantly affects the margins of large firms: An increase of 1 percentage point in leverage leads to a 1.2%
increase in the margins of large firms (Table 3, Panel A, column 7). This result is
consistent with the fact that the regulated price takes into account the cost of debt
but not the cost of equity. Therefore, firms have incentives to increase their debt
levels to negotiate higher prices with the regulator; in this way, they obtain higher
margins (Taggart (1985)). We do not find a positive effect for small firms, which
might be because small firms have less political power than large firms.
Pre-deregulation firm leverage has a negative, although not significant, effect
on the market shares of large firms (Table 3, Panel B, column 7). In this case,
debt does not lead large firms to compete more aggressively to gain market share,
as is the case after deregulation; debt is used only to manipulate the regulated
price. In fact, the higher debt level may lead these firms to pass up more efficient
investments (Myers (1977)) and maybe even lose customers (Titman (1984)),
hence the reduction in market shares.20
For all regressions in Table 3, the remaining covariates behave as expected (see Section V.A). This table also reports the Cragg–Donald statistics and
the results of the Sargan test of our IV specifications. In all regressions, our instruments pass the validity and exogeneity tests.
The previous results indicate that the effect of leverage on firms’ strategic
behavior depends on whether they operate in a regulated or competitive market.
These results also indicate that the strategic behavior of large firms is remarkably
different from that of small firms. We explore the nature of such behaviors further
in the next section.
18
As noted by Strauss-Kahn and Traca (2004).
Phillips (1995) finds similar results, but the differential effect is at the industry level. In that
paper, in three of the four industries studied, higher leverage leads firms to compete less aggressively.
In contrast, in the fourth industry, more levered firms compete more aggressively.
20
Alternatively, we could think that regulated firms are competing as predicted by Chevalier and
Scharfstein (1996) and Dasgupta and Titman (1998). However, in regulated markets, firms are protected from bankruptcy, and hence such behavior is unlikely to take place.
19
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https://doi.org/10.1017/S0022109016000673
Reboul and Toldrà-Simats
VI.
Journal of Financial and Quantitative Analysis
The Different Strategic Behavior of Large and Small Firms
The analysis in Section V shows that leverage has a different effect on the
behavior of large and small firms. The aim of this section is to understand the
market strategies that large and small firms undertake to face the increased competition in the deregulated markets. We follow other papers in the field, namely,
Phillips (1995) and Zingales (1998), and study the effect of leverage on prices and
on investment.
To make sure that our results are driven by leverage and not by firms’ heterogeneity, such as differences in their quality, we control for factors such as growth
opportunities, efficiency, size, and profitability in our regressions. We control for
year and firm fixed effects in the regressions where the dependent variable corresponds to industrial prices, and we cluster errors at the country level.21 We control
for firm fixed effects and an interacted country-year fixed effect in the investment
regressions. We adopt the same instrumentation strategy as in the previous section.
A.
Prices
We collect data on industrial prices at the market level from Eurostat. Generally, deregulation has a negative and significant impact on prices (see Table 2).
The results of a simple t-test show that after deregulation, prices fall by 1 percentage point (i.e., by 14.5% relative to the mean). This decrease is significant at
the 1% level and suggests that deregulation was effective in opening the market
to competition. Table 4 shows the results of the OLS and IV methods where the
left-hand-side variable is industrial prices and is explained by leverage, its interaction with the deregulation dummy, and the usual covariates. All the explanatory
variables are lagged 1 period.
Deregulation leads more levered firms to behave strategically to affect prices,
and this effect is different for large and small firms. In markets with more large
firms, deregulation leads to firm leverage having a negative impact on prices.
When the leverage of large firms increases by 1 standard deviation, prices decrease by about 7.2% relative to the mean price (Table 4, columns 3 and 6). This
result is consistent with the previous findings that more levered large firms increase their output and put downward pressure on prices, which results in them
having lower margins but larger market shares.
In markets with more small firms, deregulation causes more levered firms
to behave strategically to raise prices (Table 4, columns 4 and 7). Specifically, a
1-standard-deviation increase in leverage increases prices by 5.1%.22 This result
is consistent with the previous findings that the strategy of small firms is to push
prices up to increase their profits at the expense of future market shares.
21
We cannot include an interacted country-year fixed effect in the industrial price regressions because in these regressions, the dependent variable, prices, is measured at the country level.
22
The increase of 5.1% is computed using the OLS coefficient of column 4 in Table 4. The Cragg–
Donald statistic in the regression of column 7 is below 10, suggesting that in this specification, our
instruments are weak. This is the reason why the post-deregulation coefficient in this regression is 100
times larger than the OLS coefficient. Even though both coefficients have the same sign, we believe
the IV coefficient in this regression should be interpreted with caution.
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https://doi.org/10.1017/S0022109016000673
1624
TABLE 4
Effect of Leverage on Prices
Table 4 shows the effect of firm leverage on the industrial prices in each market before and after deregulation using OLS and IV methodologies. The regressions include the usual covariates, and all explanatory
variables are lagged 1 period. In column 1, we include all the firms in the sample; the rest of columns include only survivor firms. Small and large survivor firms correspond to the subsample of firms that
have total assets below and above the median of 1.5 million euros, respectively. We include firm, year, and country fixed effects. Standard errors (shown in parentheses) are robust to heteroskedasticity and
autocorrelation. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the Appendix.
Industrial Prices
R2
No. of obs.
OLS Survivors
OLS Large Survivors
OLS Small Survivors
IV Survivors
IV Large Survivors
1
2
3
4
5
6
−0.008 (0.002)***
0.006 (0.002)**
—
—
—
—
—
—
—
—
0.77
904
First-Stage Regression Results
FIXED_ASSETS
FIXED_ASSETS × DEREG
INVENTORIES
INVENTORIES × DEREG
0.92
507
0.81
507
−0.021 (0.006)***
0.013 (0.006)*
−0.004 (0.01)
−0.003 (0.002)*
0.02 (0.02)
0.007 (0.001)***
0.03 (0.01)**
−0.001 (0.002)
—
—
0.83
266
0.02 (0.007)***
−0.006 (0.007)
−0.014 (0.013)
−0.001 (0.0006)**
−0.01 (0.01)
−0.0009 (0.001)
−0.006 (0.003)**
0.001 (0.003)
—
—
0.83
241
−0.017 (0.006)***
0.016 (0.005)***
−0.011 (0.008)
−0.002 (0.0007)***
−0.01 (0.01)*
0.003 (0.0009)***
0.0009 (0.003)
0.0003 (0.002)
54.0
19.5 (0.00)
0.82
507
−0.028 (0.008)***
0.018 (0.007)**
−0.01 (0.01)
−0.003 (0.002)*
0.008 (0.02)
0.008 (0.001)***
0.03 (0.01)***
−0.00001 (0.00002)
46.9
2.9 (0.22)
0.84
266
7
0.2 (0.07)***
−0.16 (0.06)***
−0.01 (0.02)
−0.001 (0.001)
0.0004 (0.02)
−0.001 (0.002)
−0.013 (0.006)**
0.007 (0.007)
5
0.13 (0.9)
0.83
241
1625
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https://doi.org/10.1017/S0022109016000673
R2
No. of obs.
−0.26 (0.05)***
0.38 (0.04)***
0.06 (0.11)
−0.02 (0.12)
−0.007 (0.004)**
0.009 (0.004)**
−0.008 (0.008)
−0.001 (0.0007)***
−0.01 (0.01)
0.002 (0.0009)***
−0.0008 (0.003)
0.001 (0.002)
—
—
IV Small Survivors
Reboul and Toldrà-Simats
LEVERAGE × DEREG
LEVERAGE
INTANGIBLES
GROWTH_OPPORTUNITIES
PROFITABILITY
SIZE
CASH
ROIC (×100)
CRAGG_DONALD_STAT
SARGAN_TEST
OLS All
Journal of Financial and Quantitative Analysis
Firm leverage before deregulation has a positive effect on prices in markets
with more large firms, consistent with the finding of Taggart (1985) that regulated
prices take into account firms’ cost of debt.
B.
Investment
Table 5 shows the results of several regression specifications where the dependent variable corresponds to firm investment as explained by firm leverage, its
interaction with deregulation, and the rest of our usual covariates.
As shown in column 1 of Table 5, post-deregulation firm leverage has a positive effect on investment. Columns 3 and 7 show that this effect is actually driven
by the largest firms (i.e., firms in the 75th percentile). For these firms, an increase
of 1 percentage point in leverage increases their investment by over 3%. This result is consistent with our previous findings: Deregulation induces more levered
large firms to compete more aggressively by increasing output. A natural way
for firms to increase output is to invest in cost-reducing technologies or in capacity expansion, for example, by acquiring other firms. In contrast, small firms
with more leverage invest significantly less, consistent with their output-reducing
strategy.
Pre-deregulation firm leverage does not seem to have an effect on investment;
if anything, it harms small firms’ ability to invest (Table 5, column 4). This is in
accordance with the decrease in these firms’ margins before deregulation (Table 3,
Panel A) and with the Myers (1977) underinvestment problem.
VII.
What Drives the Difference in the Behavior of Large and
Small Firms?
The goal of this section is to determine the fundamental conditions of large
and small firms that induce them to react differently to deregulation. In particular, we want to know why more leverage leads large firms to increase output, as
predicted by Brander and Lewis (1986), whereas more leverage leads small firms
to pursue short-term gains by increasing their margins at the expense of market
share, as noted by Chevalier and Scharfstein (1996) and Dasgupta and Titman
(1998). We explore several possible explanations.
A.
Efficiency
The first potential explanation for the difference in the behavior of large and
small firms is that large firms are more efficient, and their greater efficiency allows
them to expand capacity at a lower cost. To find out whether this conjecture is true,
we use various efficiency ratios: asset turnover (sales over total assets), inventory
turnover (sales over inventories), and receivables turnover (sales over accounts
receivable). Table 6 shows the results of univariate regressions including all the
firms in the sample.
As shown in Table 6, none of the efficiency measures is positively correlated with firm size. The negative coefficient of size on pre-deregulation inventory
turnover implies that, if anything, larger firms are less efficient before deregulation. We conclude that efficiency considerations are not driving the differential
behavior between large and small firms.
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https://doi.org/10.1017/S0022109016000673
1626
TABLE 5
Effect of Leverage on Investment
Table 5 shows the OLS and IV results of a model in which the dependent variable is firm investment and is explained by firm leverage before and after deregulation and several covariates. All explanatory
variables are lagged 1 period. Small survivor firms are those with total assets below the median of 1.5 million euros; large survivor firms have total assets above the median. The largest firms subsample includes
firms in the 75th percentile of total assets. Firm fixed effects and an interacted country-year fixed effect are included in all regressions. Standard errors (shown in parentheses) are robust to heteroskedasticity
and autocorrelation. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the Appendix.
Firm Investment
R2
No. of obs.
OLS Large Survivors
OLS Largest
OLS Small Survivors
IV Survivors
IV Large Survivors
IV Largest
1
2
3
4
5
6
7
1.9 (0.67)***
−0.97 (0.65)
−1.9 (1.01)**
0.21 (0.12)*
−5.8 (1.6)***
−0.03 (0.18)
0.47 (0.72)
0.013 (0.004)***
—
—
0.75
402
First-Stage Regression Results
FIXED_ASSETS
FIXED_ASSETS × DEREG
INVENTORIES
INVENTORIES × DEREG
0.97
402
0.90
241
1.08 (0.6)*
−0.88 (0.68)
0.6 (1.03)
0.08 (0.19)
−1.4 (2.03)
−0.25 (0.18)
−0.35 (0.91)
0.01 (0.01)
—
—
0.89
127
0.63 (3.3)
−0.96 (0.45)**
−20.3 (25)
0.44 (0.43)
−16.2 (2.7)***
0.61 (0.35)*
2.1 (2.0)
0.004 (0.009)
—
—
0.89
161
1.54 (1.26)
−0.63 (0.99)
−0.8 (1.17)
0.38 (0.17)**
−6.0 (1.6)***
−0.009 (0.2)
0.7 (0.8)
0.01 (0.004)***
17.74
6.3 (0.1)
0.75
402
−2.1 (1.17)*
0.7 (1.8)
0.31 (3.6)
0.27 (0.15)*
−4.5 (1.4)***
−0.37 (0.65)
−1.8 (2.0)
0.03 (0.01)***
16.58
4.5 (0.20)
0.90
241
3.1 (1.3)**
−0.83 (0.7)
−8.0 (1.8)***
0.57 (0.34)*
2.52 (4.2)
0.17 (0.39)
21.2 (2.7)***
0.04 (0.03)
9.44
1.7 (0.18)
0.97
127
8
−5.3 (3.1)*
2.7 (2.2)
−30.8 (9.7)***
0.41 (0.34)
−18.8 (3.37)
0.5 (0.6)
2.6 (1.22)**
0.002 (0.006)
8.25
6.6 (0.0)
0.89
161
1627
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R2
No. of obs.
−0.27 (0.06)***
0.35 (0.05)***
−0.4 (0.17)**
0.45 (0.15)***
−0.5 (2.0)
0.31 (0.5)
−0.55 (3.6)
0.17 (0.17)
−5.1 (2.4)**
−0.6 (0.67)
−2.1 (1.9)
0.03 (0.01)***
—
—
IV Small Survivors
Reboul and Toldrà-Simats
LEVERAGE × DEREG
LEVERAGE
INTANGIBLES
GROWTH_OPPORTUNITIES
PROFITABILITY
SIZE
CASH
ROIC
CRAGG_DONALD_STAT
SARGAN_TEST
OLS Survivors
Journal of Financial and Quantitative Analysis
TABLE 6
Efficiency of Large and Small Firms
Table 6 shows univariate regressions of the effect of firm size on three efficiency ratios before and after deregulation: asset
turnover, inventory turnover, and receivables turnover. Firm fixed effects and an interacted country-year fixed effect are
included in all regressions. Standard errors (shown in parentheses) are robust to heteroskedasticity and autocorrelation.
*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the
Appendix.
Asset Turnover
Total assets (in millions)
Firm fixed effects
Country-year fixed effects
R2
No. of obs.
B.
Inventory Turnover
Before Dereg.
After Dereg.
Before Dereg.
−0.0009
(0.001)
0.005
(0.003)
Yes
Yes
Yes
Yes
Yes
Yes
0.92
474
0.81
473
0.72
439
−0.9
(0.4)**
Receivables Turnover
After Dereg.
Before Dereg.
After Dereg.
8.2
(7.2)
0.06
(0.22)
0.02
(0.02)
Yes
Yes
Yes
Yes
Yes
Yes
0.28
433
0.29
474
0.69
471
Excess Capacity
Another reason why large firms are able to adjust their output might be because their excess capacity is larger than that of small firms. Unfortunately, we
do not possess data on excess capacity at the firm level to test this explanation.
However, although it is possible that large firms have more excess capacity, our
previous results show that large firms increase their investment, whereas small
firms do not. Such result suggests that excess capacity is not the only explanation for the increased production of large levered firms because these firms are
expanding beyond their pre-deregulation level of installed capacity.
C.
Financial Constraints
A way for firms to increase their investment is by borrowing external
funds (72% of the large firms and 48% of small firms in our sample have a
ratio of cash to capital expenditures below 1). Large firms may behave differently than small firms because they have better access to capital markets and thus
are more able to pursue aggressive strategies by investing in additional capacity.
We use three different measures of financing constraints: bond ratings that we
hand-collected from Moody’s Web site (https://www.moodys.com/), the Kaplan–
Zingales (KZ) index, and the Whited–Wu (WW) index.23 Higher bond ratings
imply lower financial constraints, whereas higher scores on the KZ index and the
WW index indicate higher financial constraints.
Table 7 shows univariate regressions on the correlation between size and
financing constraints of firms before and after deregulation, including all firms
in our sample. All regressions contain firm fixed effects and a country-year fixed
effect, as well as robust standard errors.
In all regressions, the variable TOTAL ASSETS has a negative and significant impact on firms’ financing constraints before deregulation, which suggests
23
We define the KZ index as used by Lamont, Polk, and Saá-Requejo (2001) and several following
papers. KZ index = −1.002 × Cash flows + 0.283 × Tobin’s Q + 3.319 × Total debt − 39.368 ×
Total dividends − 1.315 × Cash. We build the WW index as follows: WW index = −0.091 × ((Cash
flow/Sales) × Net sales or revenues) − 0.062 × Dividend dummy + 0.021 × Long-term debt + 0.102
× Industry sales growth − 0.035 × Sales growth. We modified the original WW index by excluding
the variable SIZE because this variable is an explanatory variable in our regression.
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1628
1629
TABLE 7
Financial Constraints of Large and Small Firms
Table 7 shows the results of univariate regressions of the effect of firm size on three measures of financial constraints
before and after deregulation: companies’ credit ratings, the Kaplan–Zingales index, and the Whited–Wu index. In the
regressions where the dependent variable corresponds to ratings, total assets are in millions of dollars; otherwise, total
assets are in dollar amounts. Firm fixed effects and an interacted country-year fixed effect are included in all regressions. Standard errors (shown in parentheses) are robust to heteroskedasticity and autocorrelation. *, **, and *** indicate
significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the Appendix.
Ratings
Total assets
Firm fixed effects
Country-year fixed effects
R2
No. of obs.
Kaplan–Zingales index
Whited–Wu index
Before Dereg.
After Dereg.
Before Dereg.
After Dereg.
Before Dereg.
0.074*
(0.03)
0.0005
(0.02)
−5.4**
(2.6)
−4.4
(3.53)
−0.2*
(0.06)
After Dereg.
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.99
34
0.95
86
0.97
318
0.95
304
0.98
406
0.96
430
−0.5***
(0.05)
that large firms have more access to capital markets than small firms when deregulation takes place.24
In an unreported set of regressions, we are able to reproduce our main results
from Section V by dividing the sample into more or less financially constrained
firms instead of dividing it into large and small firms. The results of these regressions show that financial constraints are an important determinant of the difference
in the strategies of large and small firms. More specifically, large firms are less financially constrained than small firms, and this explains why more aggressive
competitive behavior à la Brander and Lewis (1986), where firms raise external
funds to expand, makes more sense for large firms.25
D.
Financial Distress
The key argument of Chevalier and Scharfstein (1996) and Dasgupta and Titman (1998) is that more levered firms have a higher probability of going bankrupt
and thus have a higher discount rate. Higher discounting leads firms to increase
current profits at the expense of future market share. A possible explanation for
why the logic of this argument might apply to small firms more than to large firms
is that bankruptcy might be a more likely event for small firms.
The results presented in the previous section reveal that small firms have
greater financial constraints at the onset of deregulation, and financial constraints
might be considered as a proxy for financial distress. In this section, we use
Altman’s Z -score as an additional measure of financial distress.26 Table 8 shows
24
After deregulation, large firms no longer enjoy better access to financial markets than small firms.
This effect might be driven by the more levered large firms, which adopt riskier strategies, in line with
Brander and Lewis (1986). Such strategies make investors more reluctant to provide funds.
25
Phillips (1995) uses a parallel argument to explain why, in three of the four industries studied
in his paper, levered firms cut investment and increase prices. He explains that in these industries,
investment requires a substantial amount of funds, but these funds are difficult to obtain when firms
have more debt. In the fourth industry, firms with more debt invest more. Phillips explains that this is
because in that industry, expansion is easy and low cost. In our context, capacity expansion has a similar cost for all firms (without taking into account the cost of borrowing); therefore, what differentiates
the ability of large firms to expand is their better access to external funds.
26
Altman’s Z -score is calculated as 3.3X 1 + 1.4X 2 + X 3 + 1.2X 4 + 0.6X 5 , where X 1 is the ratio of
the firm’s earnings before interest and taxes to total assets, X 2 corresponds to the ratio of retained
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Reboul and Toldrà-Simats
Journal of Financial and Quantitative Analysis
TABLE 8
Probability of Bankruptcy of Large and Small Firms
Table 8 shows univariate regressions of the effect of firm size on the probability of bankruptcy as measured by Altman’s
Z -score. Firm fixed effects and an interacted country-year fixed effect are included in all regressions. Standard errors (shown in parentheses) are robust to heteroskedasticity and autocorrelation. *, **, and *** indicate significance at
the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the Appendix.
Altman’s Z -Score
Before Dereg.
Total assets (in millions)
Firm fixed effects
Country-year fixed effects
R2
No. of obs.
−0.08
(0.03)***
Yes
Yes
0.84
315
After Dereg.
0.05
(0.1)
Yes
Yes
0.73
341
two univariate regressions for the relationship between the size of firms and the
distress measure.
The negative sign of the coefficient means that, according to their accounting information, small firms have a higher probability of going bankrupt before
deregulation. Even though companies were protected from going bankrupt before
deregulation, this result means that small firms became exposed to bankruptcy
when governments removed regulation. This result also explains why small firms
tried to boost current profits when deregulation occurred.
After deregulation, the effect still has the same sign but is not significant.
This effect is due to the bankruptcy and exit of small firms. If we examine the
size of the firms that exit in our sample when deregulation occurred, we find that
the 13 firms that exit are significantly smaller than the average survivor firm: Exiting firms have an average of 2 million in assets, whereas permanent firms have
an average of 8.5 million in assets (t-statistic = 3.33). This fact provides further
support for the idea that small firms are closer to bankruptcy than large firms.
Moreover, as we will see in Section VIII, regulation gives firms incentives to take
excessive debt. Also, as we saw before, pre-deregulation leverage reduces small
firms’ ability to invest (Section VI.B) and their margins (Section V.B.1). These
results and the previous one highlight a perverse incentive of regulation: The excessive debt reduces firms’ ability to invest and remain competitive during the
regulated period, and increases their likelihood of exit when deregulation occurs.
VIII.
The Effect of Deregulation on Leverage
In Section IV, we used descriptive statistics to show that deregulation significantly modified firms’ competitive environment and their exposure to financial
distress. In Sections V–VII, we showed that levered firms modified their strategic
behavior after deregulation to affect their competitive position in the market. In
this section, we explore whether firms modified their leverage following deregulation both to reduce their chances of going bankrupt and as part of their strategy
to affect their competitive outcome.
earnings to total assets, X 3 is the ratio of sales to total assets, X 4 is the ratio of working capital to
total assets, and X 5 is the ratio of market value of equity to total liabilities. A higher Z -score means a
higher probability of distress.
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1630
A.
1631
Empirical Strategy
We use the year of deregulation as a natural experiment to explore the causal
effect of competition on the leverage decisions of firms. We can conduct such a
natural experiment because deregulation is an exogenous event for each individual
country because it stems from legislation at the European level.27
The structure of our sample makes it possible to use a difference-indifference strategy. Our data set includes firm-level observations in different countries that are all subject to the deregulation directive but are affected in different
ways by its adoption. The year/country cells belong to three different groups:
i) those countries that are not subject to deregulation because they are deregulated
at the start of the period (UK) or are not subject to the EU directive during the
period (Portugal), ii) those countries that are subject to deregulation but have not
yet put it in practice (all the rest of the sample before the year, specific to each
country, in which the directive was assimilated in the nation’s law), and iii) the
same countries after deregulation was put into force.
The fact that deregulation is not simultaneous in each country forces a slight
modification in the usual difference-in-difference method.28 We run the following
regression:
(3)
LEVitj
=
1treat j β3 + (1treat j × 1dereg jt )β4 + X i,t−1 γ3 + λt + λi + vit .
For firm i in country j at time t, the dependent variable LEVitj is leverage and
is explained by the treatment variables (1treat j and (1treat j × 1dereg jt )), firm-specific
characteristics X i,t−1 , common time effects across Europe λt , and firm-level heterogeneity λi . Errors are robust and clustered at the country level. The first treatment variable (1treat j ) is a dummy that takes the value of 1 for firms that operate in
a market that deregulated during the sample period, and 0 for firms that operate in
27
Given that EU members enjoyed some discretion in the timing of the transposition of deregulatory measures, we may question whether deregulation was, in fact, an unexpected event. We are
confident that deregulation in our sample is exogenous for the following reasons: First, the deregulation of electricity markets is part of the European integration process; hence, all member countries
have to comply with the measures by the specified deadlines. Second, a number of studies show that
delays longer than 6 months in implementing the EU directives are attributable to cross-national factors, such as the political priority assigned to the process, the timing of national elections, the political
power in the European Council of Ministers, the length of membership in the EU, and the economic
power of the member state, rather than idiosyncratic characteristics related to a particular law (see
Mbaye (2001), Falkner, Hartlapp, Leiber, and Treib (2005), and Konig and Luetgert (2008)). A third
argument for the exogeneity of deregulation is suggested by data on delays in the transposition of
several other EU directives by member countries. We obtained information for the 13 countries in our
sample for the period 1986–2002 from Celex (Sector 7), a database on the legal issues of the EU.
Transposition delays vary widely across countries, ranging from 8% of directives delayed in Sweden
to 51% of directives delayed in Portugal. If we rank member states according to the percentage of
delays from low to high and compare the ranking to the year of transposition of the EU electricity
directives by each member state, we find that the transposition of the electricity directive is no exception: countries that have traditionally had the highest (lowest) percentage of delays in implementing
EU directives are also the latest (earliest) ones to implement the electricity directive.
28
Table 1 reports the country and year in which deregulation took place. When there was some
ambiguity about the exact timing of deregulation, usually because of a gradual opening process, we
chose the last year in which the minimal requirements of the directive were implemented as the year of
deregulation. This choice is, of course, somewhat arbitrary, but because our methodology gives weight
to long differences, we are confident that the results are not sensitive to it.
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Reboul and Toldrà-Simats
Journal of Financial and Quantitative Analysis
a market that did not deregulate. The variable 1dereg jt is the deregulation dummy.
The coefficient β3 captures the permanent differences in the financing decisions
of unregulated countries and countries that end up deregulating. The coefficient
β4 is the difference-in-difference estimator, and it captures the differences in the
financing decisions of firms operating in unregulated countries or firms in countries that end up deregulating before deregulation occurs, and firms in countries
that end up deregulating after deregulation takes place. Thus, the coefficient β4
captures the deregulation effect.
The vector X i,t−1 includes (lagged) firm characteristics such as growth
opportunities, profitability, size, tangible assets, the return on invested capital, and
cash to control for additional determinants of firms’ capital structure choices.
We expect firms’ leverage ratio to vary inversely with growth opportunities for the following reasons: First, firms with more growth opportunities suffer more from the underinvestment problem (Myers (1977)); second, firms with
more growth opportunities face lower agency problems of free cash flow (Jensen
(1986)). Profitability and the return on invested capital should enter the regressions with a negative sign, as predicted by the pecking order theory (Myers and
Majluf (1984)). Size should be positively related to leverage because larger firms
have higher debt capacity. Tangible assets should be positively correlated with
leverage because tangible assets can be used as collateral. Finally, cash should
display a negative sign because it is a substitute for leverage.
B.
Regression Results
The results of our baseline specification are shown in Table 9.
As leverage measures, we use the ratio of the book value of debt to the book
value of assets (regressions 1–3 in Table 9) and the ratio of the book value of
debt to the market value of assets (regressions 4–6) for robustness. Regressions 1
and 4 omit the covariates. In regressions 2, 3, 5, and 6, we add the controls; in
regressions 3 and 6, we include only the permanent firms in the sample to make
sure that our results are not driven by the entry and exit of firms.
Survivor firms in regulated countries that end up deregulating have higher
leverage than firms in unregulated countries (columns 3 and 6 in Table 9). After
deregulation, the ratio of debt to book value of assets significantly decreased by
8 percentage points, and the ratio of debt to market value of assets significantly
decreased by a similar magnitude. Thus, the opening of the European electricity
markets to competition prompted firms to significantly decrease leverage. This
provides evidence that in regulated industries, firms choose higher debt ratios than
in competitive industries, and that firms reduce debt to mitigate the probability of
going bankrupt and affect their survival in the market once competition starts.
The additional controls show that firm-level heterogeneity is pervasive. All
covariates have the expected sign.
C.
Robustness
Various robustness checks support the validity of our previous results. We
estimate the previous model without imposing the restriction that deregulation
should have the same effect across all European countries (estimation results are
available from the authors). We find that leverage decreases after deregulation
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1632
TABLE 9
Effect of Deregulation on Firm Leverage
Table 9 presents the effect of deregulation on firms’ capital structure. The dependent variable is firm leverage. It corresponds to the debt-to-book value of the assets in columns 1–3, and it is the debt-to-market
value of the assets in columns 4–6. The independent variables are our difference-in-difference estimator and the balance sheet variables used in the previous analysis. We include firm and year fixed effects.
Standard errors (shown in parentheses) are robust to heteroskedasticity and autocorrelation and are clustered at the country level. All regressions use all observations in our sample except columns 3 and 6,
which include only the permanent firms in our sample. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. All variable definitions are provided in the Appendix.
Debt-to-Book Value of Assets
Debt-to-Market Value of Assets
All Firms
All Firms
Survivor Firms
All Firms
All Firms
1
2
3
4
5
Survivor Firms
6
DEREGULATES
−0.18
(0.02)***
0.14
(0.08)*
0.27
(0.07)***
−0.24
(0.05)***
−0.61
(0.26)
0.10
(0.05)**
DEREGULATION_EFFECT
−0.06
(0.01)***
−0.07
(0.02)***
−0.07
(0.02)***
−0.15
(0.03)***
−0.08
(0.02)***
−0.07
(0.02)***
—
0.01
(0.008)
0.018
(0.007)**
—
−0.09
(0.03)***
−0.09
(0.04)**
PROFITABILITY
—
−0.34
(0.21)*
−0.87
(0.16)***
—
−0.99
(0.27)***
−1.3
(0.27)***
SIZE
—
0.05
(0.01)***
0.06
(0.01)***
—
0.08
(0.02)***
0.08
(0.03)***
FIXED_ASSETS
—
0.29
(0.05)***
0.3
(0.05)***
—
0.13
(0.16)
0.12
(0.19)
INVENTORIES
—
−0.35
(0.19)*
0.12
(0.09)
—
−0.22
(0.21)
−0.05
(0.26)
ROIC
—
−0.001
(0.0005)**
−0.001
(0.0003)***
—
CASH
—
−0.13
(0.04)***
−0.06
(0.04)*
—
−0.07
(0.11)
−0.02
(0.12)
0.83
555
0.57
698
0.67
671
0.65
540
0.68
947
0.78
690
−0.003
(0.0009)***
1633
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https://doi.org/10.1017/S0022109016000673
R2
No. of obs.
0.003
(0.0008)***
Reboul and Toldrà-Simats
GROWTH_OPPORTUNITIES
Journal of Financial and Quantitative Analysis
for the majority of countries. In another regression (not reported), we find that
the deregulation effect on leverage becomes stronger when we exclude Portugal,
Greece, France, and Italy, the countries that the European Commission identified
as the slowest in adopting deregulation.
Given our cross-country analysis, we also need to consider the extent of
firms’ cross-border activity. If cross-border electricity flows were substantial, our
deregulation measure would not effectively capture the true exposure to competition of these firms. Fortunately for our study, congestion of the cross-border
interconnection occurs very frequently during our sample period. When this
happens, price convergence is not possible, and the neighbor electricity markets
are separated.29 In fact, the DG TREN30 reports that cross-border electricity flows
were only 8% in 2000 and increased to only 10.7% 5 years after that.31 Therefore, cross-border integration in the electricity markets during our sample period
is weak.
Finally, in a number of cases, firms that have not deregulated their industry hold shares in foreign firms that already operate in a deregulated environment. However, although firms hold more financial assets after deregulation relative to before, the average financial assets of firms during our sample period do
not explode, which is what we would expect if cross-border participations were
widespread.
IX.
Conclusion
This paper presents evidence that leverage induces firms to behave strategically in competitive product markets and emphasizes the differential strategic behavior of large and small firms. We exploit the deregulation of the European electricity markets to explore how levered firms modify their behavior after the competition shock brought by liberalization. After deregulation, large levered firms
adopt aggressive output strategies by investing more. This strategy allows these
firms to increase their market shares. However, the larger electricity supply puts
downward pressure on prices, which leads to a reduction in the profit margins
of large firms. In contrast, the strategy of small levered firms is to pursue higher
margins, even though this comes at the cost of losing market share.
We study the conditions of large and small firms that induce them to adopt
such different strategies. We provide evidence that large firms are more able to
raise external funds than smaller firms. We conclude that their greater access to
external funds is the reason why the theory of Brander and Lewis (1986) better
explains the expansion behavior of large firms. We also find that small firms are
significantly closer to financial distress than large firms upon deregulation. We
conclude that their greater probability of bankruptcy is the reason why the theories
of Bolton and Scharfstein (1990) and Dasgupta and Titman (1998) best explain the
behavior of small firms. Based on our findings on the differences in the product
market strategies of large and small producers, we hope to open a new line of
29
See Bosco, Parisio, Pelagatti, and Baldi (2006).
Directorate General of Transport and Energy.
31
DG TREN Benchmarking report (2005), Technical Annex, p. 24.
30
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1634
1635
future research on a currently underexplored variable in finance, which is the size
of firms.
We also highlight some negative effects of regulation. Specifically, under regulation, firms are encouraged to increase debt to benefit from the higher prices set
by the regulator. However, higher leverage reduces firms’ ability to invest during
the regulated period and also their market shares. The higher leverage also reduces
firms’ ability to compete once deregulation takes place. This is especially true for
small firms, which have a higher probability of exiting the market. The exit of
small firms after deregulation goes against the initial purpose of liberalization,
which is to reduce industry concentration.
Appendix. Variable Definitions
This Appendix presents the definitions of the variables used throughout the paper. All
variables are at the firm level except for prices, which are at the market level.
LEVERAGE: Total debt/Total assets.
SIZE: ln(Total assets).
GROWTH OPPORTUNITIES: Enterprise value/Total assets.
PROFITABILITY: Operating income/Total assets.
INTANGIBLES: Intangible assets/Total assets.
CASH: Cash/Total assets.
ROIC: (Net Income − Dividends)/Total capital.
FIXED ASSETS: Property, plant, and equipment/Total assets.
INVENTORIES: Inventories/Total assets.
PROFIT MARGINS: Operating income/Net revenues from sales.
MARKET SHARES: Firm sales/Total market sales.
INVESTMENT: ln(Capital expenditures/Total assets).
PRICES: Industrial prices in each market (from Eurostat).
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