Journal of Corporate Finance 17 (2011) 1016–1027
Contents lists available at ScienceDirect
Journal of Corporate Finance
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j c o r p f i n
How do managerial successions shape corporate financial policies in
family firms?
Mario Daniele Amore a,⁎, Alessandro Minichilli b, Guido Corbetta b
a
Copenhagen Business School, Department of Economics, Porcelænshaven 16 A1, 2000, Frederiksberg, Denmark
Bocconi University, Department of Management & Technology, AIdAF-Alberto Falck Chair of Strategic Management in Family Business,
Via Roentgen 1, 20136, Milan, Italy
b
a r t i c l e
i n f o
Article history:
Received 27 June 2010
Received in revised form 30 April 2011
Accepted 6 May 2011
Available online 14 May 2011
JEL classification:
G3
G32
Keywords:
Family firms
CEO succession
Professional managers
Financial policies
Financial flexibility
a b s t r a c t
Despite recent evidence on the importance of chief executive officer (CEO) successions in
family firms, we still know little about the differences in corporate strategies entailed by family
and professional managers around transition. We investigate the consequences of managerial
successions for the financial policies of Italian family firms. Our findings indicate that the
appointment of non-family professional CEOs leads to a significant increase in the use of debt,
primarily driven by short-term maturities. We document substantial heterogeneity in the
impact of professional successions on debt financing: the increase in debt is particularly
pronounced for young firms, firms with a high level of investment, and firms in which the
controlling family maintains a dominant representation on the board of directors. Examining
the importance of financial flexibility, we find that the increase in debt occurs primarily when
firms are cash-poor, and when incoming CEOs can exploit spare borrowing capacity.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Top executives have long been considered a key determinant of a firm's strategies (Bertrand and Shoar, 2003) and ultimately of
its corporate performance (Bennedsen et al., 2009; Hambrick, 2007; Hambrick and Mason, 1984). As such, the selection of a new
CEO represents one of the most critical decisions for a firm's future direction and effectiveness (Shen and Cannella, 2002, 2003;
Zhang and Rajagopalan, 2004, 2010).
Because of the role played by family ties, personal objectives and conflicts on a firm's organization and governance (Bertrand et al.,
2008; Bertrand and Shoar, 2006), the choice of appointing either a family or a professional CEO has acquired special meaning in family
firms. On the one hand, the typical overlap between executive and ownership positions at the apex of families makes successions a
traumatic moment (Gomez-Mejia et al., 2001) posing a threat to factors such as longer investment horizons, reputational concerns and
diminished agency conflicts between managers and owners, which often lead to superior performance compared to non-family firms
(Anderson and Reeb, 2003a; Andres, 2008; Maury, 2006; Sraer and Thesmar, 2007). On the other hand, naming a family heir to enjoy
the private benefits of control might be an inferior decision in terms of managerial talent (Perez-Gonzales, 2006), inducing lower
performance (Villalonga and Amit, 2006) and productivity (Barth et al., 2005).1
Motivated by such arguments, a growing literature compares the impact of incoming family heirs and professional CEOs on
firm performance around transition (Bennedsen et al., 2007; Cucculelli and Micucci, 2008; Perez-Gonzales, 2006). While this
⁎ Corresponding author. Tel.: + 45 3815 2352; fax: + 45 3815 2576.
E-mail addresses: [email protected] (M.D. Amore), [email protected] (A. Minichilli), [email protected] (G. Corbetta).
1
Revisiting this strand of research, Miller et al. (2007) argue that whether family firms outperform non-family firms hinges crucially on the definition of family
firm and the governance variables considered.
0929-1199/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jcorpfin.2011.05.002
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
1017
evidence demonstrates that successions outside the family are typically associated with an improvement in operating returns, we
still know little about the differences in decision-making between family and professional CEOs in family firms.
In this paper, we start by testing the differences between family and blood-unrelated professional CEOs on corporate financial
policies around transition. We then investigate how firm and governance characteristics matter in shaping the effect of
professional CEO appointments on financial policies. Finally, by employing professional successions to identify changes in a firm's
investment opportunity set, we examine in the context of family firms the recent notion that financial flexibility and unused debt
capacity are helpful to enhance investment ability and firm performance through subsequent debt financing (Denis and McKeon,
2010; Mura and Marchica, 2010). The empirical analysis is conducted on a unique dataset covering listed and unlisted Italian
family-held companies. Despite family's influence on the corporate sector in Italy is pervasive (Faccio and Lang, 2002; La Porta
et al., 1999), Italian family firms remain significantly underexplored, mainly because of the lack of reliable data for privately-held
firms. 2 Yet, Italy represents a unique setting to investigate family firms and, in particular, their capital structure decisions. While
family firms in other institutional settings have been found to maintain a low leverage (see Bach, 2010 for France and Bennedsen
et al., forthcoming for Denmark), anecdotic evidence indicates that Italian family businesses have historically adopted a high-debt
policy as a source of financing. For example, The Economist (March 2nd, 2000) writes that “Typically, Italian entrepreneurs have
been loth to surrender even a small part of their equity capital to stock market investors. Instead, financing came from cash flow or
bank loans”. 3 This picture is in line with cross-country evidence in Ellul (2008), who argues that families shape firms' capital
structure by trading off the need to raise external finance and the aversion to diluting control through equity issuances; debt
represents a suitable solution being a source of finance that does not dilute control.
Applying difference-in-differences models, we show that professional transitions lead to a much more aggressive debt policy:
firms controlled by families and headed by professional CEOs experience, on average, a 6.5% leverage increase around transition.
This effect is robust to the inclusion of several controls that are considered to influence debt choices. Moreover, the result remains
after we conduct several robustness tests, including the adoption of a propensity-score matching strategy (as in Cucculelli and
Micucci, 2008) to mitigate concerns about endogeneity.
Our interpretation is based on the view that CEO successions exacerbate capital structure decisions in family firms. Managers
selected from outside the family typically have superior skills (Bennedsen et al., 2007; Caselli and Gennaioli, 2005; Perez-Gonzales,
2006) and thus are better able to bring on attractive growth opportunities after the transition. Chua et al. (2003) argue that
“nonfamily managers are necessary for the firm to grow and may, in fact, accelerate that growth by providing needed skills and
new ideas”. For a family that hires a professional manager to drive company's growth while being reluctant to issue equity and
lacking enough internal resources, the use of a security that does not dilute control such as debt should be more intense. Thus, the
debt increases we document may reflect a need for funds to cope with the expansion of a firm's investment opportunity set
determined by incoming professional CEOs. In line with this interpretation, we find that, while professional CEOs foster
investment for the average firm, there is a positive and significant association between debt increases and investment around
transition. This result is consistent with recent evidence that debt increases often are a response to operating needs, mostly
associated with investment (Denis and McKeon, 2010). Also, we find that debt ratios increase more among young firms, which
typically have a high growth potential, and firms in which the family plays an active role in managerial decisions through the board
of directors. When analyzing debt maturities, we find that the increase in leverage stems from short-term debt, in line with
existing evidence (Barclay and Smith, 1995; Johnson, 2003) that shorter maturities are particularly suited for firms with growth
opportunities.
Previous research shows that excessive debt financing may ultimately expose firms to bankruptcy risk and ex-ante
underinvestment (Myers, 1977). We find that the positive impact of professional CEO successions on leverage arises primarily
among firms that attain spare debt capacity in the pre-succession period. As recently argued (Denis and Sibilkov, 2010; Faulkender
and Wang, 2006), liquidity holdings represent an alternative channel to ensure financial flexibility and enable firms to invest in
value-enhancing projects, especially when other sources of finance are too costly or not available. Consistent with this notion, we
find that, while existing liquidity does not differ between succession types, the increase in debt is higher for firms that are cashpoor in the pre-succession period.
Succession models may affect debt for reasons different than the combination of investment opportunities brought on by
professional CEOs and constraints imposed by control-motivated families on the type of funds chosen to finance growth.
From an agency perspective, leverage can be used as a device to limit managerial slack (Jensen, 1986) or informational risks
associated with non-family management, which may be relevant in a multiple agency perspective (Bruton et al., 2010;
Filatotchev et al., 2011). Also, family owners might shape leverage to limit corporate risk undertaken by professional CEOs.
In an attempt to explore these alternative hypotheses, our results show that debt increases do not significantly differ
between firms with high or low overinvestment potential, as proxied by the level of assets in place (Harvey et al., 2004)
prior to succession. Moreover, we find that debt increases do not relate to changes in profit volatility around transition, in
line with the evidence in Anderson and Reeb (2003b) that families do not seem to significantly influence a firm's capital
structure as a risk reduction strategy.
2
A few recent exceptions are Cucculelli and Micucci (2008), who collect survey data on small manufacturing firms to analyze the impact of family successions
on profitability, and Minichilli et al. (2010), who use survey data to test how top management teams contribute to family firms' performance. However, none of
these works analyzes financial flexibility and capital structure decisions.
3
More recent evidence along this line is provided by the 1st Report of the “AUB (AIdAF-Unicredit-Bocconi University) Italian Observatory on Family Firms”,
(2009).
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M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
We contribute to the existing literature in different and significant ways. Our results contribute to better understand the figure of
professional managers in family-controlled firms, which is relatively under-researched compared to its importance (Chua et al., 2003).
Following Bach (2010), and Bach and Serrano-Velarde (2010), we stress the importance of going beyond performance measures to
examine how family and professional managers operate. In particular, we extend recent works on financial flexibility and debt capacity
(Denis and McKeon, 2010; Mura and Marchica, 2010) to the context of CEO successions in family firms. By employing professional
successions to identify changes in a firm's investment opportunity set, we focus on the role played by financial flexibility and debt
financing in enhancing investment ability and firm performance. While we find that family firms do not differently manage debt in
preparation of a professional or family transition, our evidence suggests that professional successors' investment policies need to be
sustained by significant debt increases. Our evidence also suggests that the creation of spare debt capacity before undertaking a
professional succession enhances the subsequent professional CEOs' impact on firm growth. Finally, we lend support to the call for
conducting family business research in a variety of institutional settings (Cucculelli and Micucci, 2008) by complementing with
insights from Italy the recent country-level evidence on family firms' capital structure, which is still rather underexplored compared to
the research on the capital structure of widely-held corporations. For recent related contributions, see Bach (2010), who uses a large
dataset of French family firms to test a model on private benefits, firm size and risk, and Bennedsen et al. (forthcoming), who provide
descriptive evidence from Danish family firms.
The remainder of the article is organized as follows. In Section 2, we illustrate the data; in Section 3, we describe the
identification strategy and provide summary statistics; in Section 4, we show our empirical results and provide a set of robustness
checks; in Section 5, we summarize and conclude.
2. Data
2.1. Sample construction and definitions
Our analysis is based on a unique dataset covering the whole population of Italian family-controlled firms with revenues of over
Eur50 million as of 2007, as identified from public sources such as AIDA (Italian Digital Database of Companies). AIDA is the Italian
provider of Bureau van Dijk European Databases and represents the most reliable and comprehensive source of financial information for
private companies, which constitute the majority of firms in our dataset (95.9%). The threshold on firm revenues, which corresponds to a
typical large or medium-sized Italian family business, ensures that most basic data items are available. Another advantage, given the focus
of our study, is that it makes possible to identify managerial successions where the control remains inside the family.4
We complement financial data from AIDA with hand-collected data on ownership and governance characteristics from official
public filings, obtained from the Italian Chamber of Commerce. Such filings represent the most reliable source of information for
private companies in Italy. The filings are presented in the form of two separate historical reports that include all the changes in
ownership and governance structures. We record exact data on each owner and his shares in the firm, leadership structures, and
boards of directors.
Following existing studies (Anderson and Reeb, 2003a, 2003b; Barth et al., 2005), we identify family control as the fractional
equity owned by family members that allows control over the company. We define as family-controlled those private firms in
which a family owns the absolute majority (i.e. 50%) of shares. Because privately-held firms, particularly in Italy, have ownership
structures characterized by a limited number of shareholders with very large blockholdings, a 50% stake is needed to achieve
control. 5 However, this threshold is reduced to 25% for listed companies, in line with other studies that assume de facto control at
similar thresholds due to collective action problems and/or the use of control-enhancing mechanisms (see e.g. Andres, 2008).
While our definition of family firm is only based on control, we check that our results are robust to the adoption of a more
restrictive definition based on both control and management.
Overall, we identify 4251 firms that are family-controlled out of a population of 7663 companies with a turnover of over
Eur50 million. Our data are thus consistent with previous evidence on the prevalence of family ownership among Italian
corporations. For firms that are part of business groups, we apply a selection criterion to identify the within-group level in which
managerial and governance mechanisms are most important. We select the holding company for mono-business groups, and the
controlled companies for the diversified groups, defined at the two-digit industry level. This procedure also allows us to avoid
replications in the data, because e.g. the CEO and the board of directors of the different operating controlled companies in monobusiness groups are in most cases identical to that of the holding company. Following this procedure, we reduce the number of
firms from 4251 to 2484, which represent the final dataset of family-controlled firms (see Appendix for details). For each of these
firms, our dataset contains accurate ownership, management and governance information for the period 2003–2007. We also
gather financial data for the period 2000–2009 so that we can employ a three-year window around CEO transitions in a differencein-differences model. 6
4
By contrast, as argued by Cucculelli and Micucci (2008) for the Italian case, in small family businesses a managerial succession often implies a control
transfer.
5
See Bennedsen and Wolfenzon (2000) for a theoretical motivation. It should also be noted that a definition of family firm based on e.g. a 10% equity stake
would imply that most Italian companies are classified as family firms.
6
For example, if a succession occurred in 2004, we consider 2001, 2002 and 2003 as pre-succession period, and 2004, 2005 and 2006 as post-succession
period. In robustness checks, we assess the validity of our findings to the use of alternative time windows that e.g. exclude the succession year or include it in the
pre-succession period.
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
1019
This dataset displays a significant variance of different firm characteristics. In terms of size, the firms range from Eur21 billion
to Eur50 million (i.e. Eur0.05 billion) of revenues. This variation presents a much lower bound than the Fortune 500 companies
(which range from $378 billion to $4.6 billion) and is also lower than the Fortune 1000 companies (which range from $378 billion
to $1.6 billion). Similarly, the firm age varies greatly, with 56% of the firms being younger than 25 years, 44% being older than
25 years and, of these, 7% being older than 50 years. These characteristics make our data particularly suited to investigate the
heterogeneous effects of CEO successions along financial and governance characteristics, and allow for going beyond the
traditional analysis of rather homogeneous samples or specific indices that, as argued by Miller et al. (2007) for the U.S. setting,
may induce biases.
2.2. Succession decisions and governance characteristics
Tracing all offices through time, we identify a succession when there is a change in the name and surname of the CEO. To this
purpose, we consider both the formal Chief Executive Officer (Amministratore Delegato), and the Executive Chairman (Presidente
Esecutivo) for firms without a formal CEO. This choice is motivated by the fact that, in the Italian corporate governance system,
Executive Chairmen and Chief Executive Officers have similar decision-making power and thus it is not straightforward to
univocally identify the CEO. 7 Furthermore, leadership by Executive Chairmen in family firms without a formal CEO is de facto
comparable to situations in which a formal CEO is in office. We classify the family or blood-unrelated nature of CEO successions by
surname affinity with the controlling family. 8 Our dataset contains 186 CEO successions 9; 73 of them, corresponding to 39% of all
successions, are classified as blood-unrelated professional successions.
The share of professional successions is higher than the one in the sample of Italian family firms collected by Cucculelli and
Micucci (2008) (about 23%), arguably because the firms in their sample are smaller. Although the definition of family firm and CEO
succession criteria may vary greatly, we also note that the fraction of professional successions in our dataset is lower than the share
found by Perez-Gonzales (2006) in the U.S. (about 63%), Bennedsen et al. (2007) in Denmark (about 66%), and Smith and AmoakoAdu (1999) in Canada (about 58%). By contrast, our share of successions outside the family is more in line with the figure found by
Tsoutsoura (2010) in Greece (about 42%). As noted in Bennedsen et al. (2007), this picture may be consistent with the theoretical
argument proposed by Burkart et al. (2003); in countries where the investor protection is relatively weak, 10 the expropriation
potential by professional CEOs is higher and, therefore, the attractiveness of successions outside the family is lower.
The mean stake held by families in our sample is high (87.9%) and, on average, 2.9 family members have ownership stakes in
the family business. Turning our attention to the board of directors, we find that, on average, the age of a board member is 54 years,
the board size is 5.6 members, and the number of family members on the board is 2.4.
3. Identification strategy and summary statistics
Our empirical approach to estimate the impact of professional successions on debt financing is based on a difference-indifferences model (DD hereafter), pioneered by Perez-Gonzales (2006) in the context of family firms. The idea of this methodology
is that, under certain hypotheses, firms experiencing one type of succession, e.g. family successions, can serve as counterfactual
(control group) for firms experiencing professional successions (treatment group). In other words, this methodology allows for
estimating the impact of successions on firm outcomes after controlling for aggregate changes in the business environment (e.g.
macroeconomic shocks) or succession-specific shocks.
The implicit hypothesis required for the validity of such methodology is that had the professional succession not happened,
those firms would behave as firms experiencing family succession. Using counterfactual terminology, this is the equivalent of
saying that the treatment group in absence of the treatment would not be dissimilar from the control group with respect to the
outcome of interest. Whereas is impossible to formally test this hypothesis, since we never observe what the outcome would be of
firms choosing professional succession if they choose family succession, we can get some insights into its validity by comparing the
two groups before succession. If they turn out to be significantly different, the choice of a given succession model is likely to reflect
past unobserved outcomes or future firm prospects and thus endogeneity problems will threaten the causal interpretation and the
magnitude of DD estimates.
Table 1 illustrates the industry distribution of professional and family successions. We find a roughly similar distribution across
industries, except for a marginally higher fraction of family successions in trade and transportation sectors.
Table 2 summarizes the average differences in firm observable characteristics one year prior to succession. On average, firms in
the two succession groups do not statistically differ in terms of age, R&D and advertisement expenditures and cash holdings. By
7
Also, there may be firms with multiple CEOs and in these cases it is not clear how the decisional power is allocated among them. Because our data contain
information on the number of CEOs, we conduct a robustness test excluding the few firms with multiple CEOs after the succession (which represent
approximately 22% of our sample) and show that our results remain unchanged.
8
One potential drawback of this definition is that we classify in-laws as professional CEOs; however, this classification error should go against finding a
significant result.
9
Such sample size is comparable to other works in this strand of research. For example, Cucculelli and Micucci (2008) is based on 229 successions, PerezGonzales (2006) on 335 successions, and Smith and Amoako-Adu (1999) on 124 successions.
10
For example, the revised anti-director rights index computed by Djankov et al. (2008) is equal to 2 for Italy and Greece, whereas Denmark and the U.S. are
scored 4 and 3, respectively.
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M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
Table 1
Industry distribution of CEO successions.
Manufacturing
Wholesale and retail trade
Professional, scientific and technical activities
Construction
Transportation and storage
Financial and insurance activities
Other
Total
All Successions
Professional Successions
Family Successions
(1)
(2)
(3)
97
(52.2)
31
(16.7)
16
(8.6)
12
(6.4)
10
(5.4)
6
(3.2)
14
(7.5)
186
(100)
39
[40.2]
9
[29]
7
[43.8]
5
[41.7]
3
[30]
3
[50]
7
[50]
73
[39.3]
58
[59.8]
22
[71]
9
[56.2]
7
[58.3]
7
[70]
3
[50]
7
[50]
113
[60.7]
Column (1) reports in parenthesis the share of successions in each industry as a percentage of the total number of successions in the sample. Columns (2) and (3)
report in squared brackets the share of family and professional successions as a percentage of the total number of successions in each industry. Industries are
defined using the primary ATECO classification (2007), which is the national version of the European classification NACE (Rev. 2). We reclassify the category
“Other” to include as well industries with less than two firms in each succession group.
contrast, the average differences in firm size (measured by the logarithm of total assets), and return on assets (measured by
earnings before interest and taxes divided by total assets), are significant, in line with existing evidence that firms appointing
professional CEOs are larger and less profitable (Bennedsen et al., 2007). Focusing on leverage, we find that companies have a high
debt ratio (measured as total debt over the sum of total debt and book value of equity). However, we do not find significant
differences between firms that undertake a professional or family succession. This result is confirmed by comparing the average
debt in the three years prior to succession. In unreported analyses, we also compare medians or averages after trimming 1% or 5%
of observations at each tail of the leverage distribution. Results confirm that there are no ex-ante differences in the capital
structure of firms that undergo family or professional successions. Overall, this evidence suggests that family firms do not
differently manage debt in preparation of a professional or family CEO succession.
Taken together, Tables 1 and 2 indicate that firms in the two succession groups are not statistically different in leverage and
other corporate characteristics; however, there are some differences in size and return on assets, which introduce endogeneity
concerns. In the empirical analysis, we adopt two estimation strategies. First, we use OLS regressions with year and firm fixed
effects to control for time and corporate invariant unobserved heterogeneity. Second, we conduct several robustness checks and
adopt a matching strategy similar to Cucculelli and Micucci (2008) to mitigate endogeneity concerns.
Table 2
Summary statistics.
Ln Assets
Firm Age
Return on Assets
Debt/Capital
Cash Holdings
R&D Expenditures
All Successions
Family Successions
Professional Successions
Difference (3)–(2)
(1)
(2)
(3)
(4)
11.77
(0.12)
32.44
(1.50)
0.04
(0.01)
0.65
(0.02)
0.06
(0.01)
0.002
(0.001)
11.53
(0.13)
33.52
(2.06)
0.05
(0.01)
0.65
(0.02)
0.07
(0.01)
0.002
(0.001)
12.13
(0.20)
30.77
(2.12)
0.03
(0.01)
0.66
(0.02)
0.06
(0.01)
0.003
(0.001)
0.60**
(0.23)
−2.75
(3.08)
−0.02**
(0.01)
0.01
(0.03)
−0.01
(0.01)
0.001
(0.001)
This table shows average firm characteristics one year prior to CEO succession. Ln Assets is the logarithm of total assets; Firm Age is the age of the firm expressed in
years; Return on Assets is the ratio of earnings before interest and taxes to total assets; Debt/Capital is the ratio of total debt to the sum of total debt and book value
of equity; Cash Holdings represent the ratio of cash and receivables to total assets; R&D Expenditures represent the ratio of R&D and advertisement expenses to
total assets. Robust standard errors are reported in parenthesis. In Column (4), *, ** and *** denote significance at 10%, 5% and 1% respectively.
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
1021
4. Empirical results
4.1. OLS regressions
Table 3 shows OLS estimates for the impact of managerial successions on debt financing around transition. The DD coefficient is
represented by the interaction between a dummy equal to one for the post-succession period ([0, +2] years after succession) and zero for
the pre-succession period ([−3, −1] years before succession), and a dummy equal to one for professional successions and zero for family
successions. This interaction captures debt changes due to professional successions compared to that due to successions inside the family.
As in Cucculelli and Micucci (2008), we include year and firm fixed effects to control for time and corporate invariant unobserved
heterogeneity. Furthermore, the specification includes a set of time-varying firm characteristics that are typically adopted in the
leverage literature, such as firm size, profitability, fixed assets, and proxies for growth opportunities (see e.g. Bradley et al., 1984; Frank
and Goyal, 2009; Graham et al., 1998; Lang et al., 1996; Titman and Wessels, 1988). Specifically, we control for logarithm of firm age,
logarithm of total assets, net income to total assets, fixed assets to total assets, and R&D and advertisement expenditures to total assets.
Regressions are estimated on a number of firm-year observations ranged between 907 and 832 depending on the specification adopted.
Results, reported in Columns (1)–(3), show that the interaction term is positive and statistically significant at the 1% level. In
economic terms, the debt change reported in Column (3) represents a 6.5% increase over the average level. In Columns (4)–(6), we
examine debt maturities by using short-term debt as dependent variable. The interaction term is always positive, though its
statistical significance varies depending on the controls included. In economic terms, the change in short-term debt reported in
Column (6) represents an increase of about 6% over the average level.
4.2. Heterogeneous effects and alternative channels
In this section, we test how financial and governance characteristics influence the economic magnitude of debt changes around
transition. In Table 4, Columns (1) and (2), we separately analyze young (below-median age) and old (above-median age) firms.
While we find a positive and significant interaction in both subsamples, the coefficient is higher among young firms, which
typically have the most growth potential. To test the association between debt increases and investment, we split the sample into
firms that realize a particularly high or low level of industry-adjusted investment after a professional transition. Investment is
computed as the ratio of capital expenditure, R&D and advertisement expenditures in time t to start-of-year total assets; the
industry adjustment is made by subtracting from the firm value the median of the relative two-digit industry computed using all
family firms, including those that did not undergo a CEO succession. We find a significant (at 5% level) and sizeable increase in debt
for firms in the upper quartile of the investment distribution (Column 3), whereas the increase is smaller for firms in the lower
quartile of the investment distribution (Column 4).
While these findings support the view that debt is used to finance growth opportunities brought on by professional CEOs, our
results might have alternative interpretations. For example, debt may be used as a governance device (Jensen, 1986) to mitigate
managerial slack or informational risks potentially associated with non-family management. Yet, we know from existing works
that large shareholdings embody monitoring power and incentives (Shleifer and Vishny, 1997), which effectively diminish agency
Table 3
Impact of CEO successions on debt: OLS estimates.
Dependent variable:
Post*Professional Succession
Debt/Capital
Short-Term Debt/Capital
(1)
(2)
(3)
(4)
(5)
(6)
0.0331***
(0.0126)
0.0353***
(0.0122)
0.0503*
(0.0265)
0.0246*
(0.0141)
0.0260*
(0.0138)
0.0298
(0.0275)
Yes
Yes
901
Yes
Yes
901
0.0431***
(0.0130)
0.0637**
(0.0282)
−0.0080
(0.0715)
−0.6890***
(0.0907)
0.0064
(0.0764)
−0.5168
(0.6186)
Yes
Yes
832
Yes
Yes
907
Yes
Yes
907
0.0315**
(0.0153)
0.0537*
(0.0294)
0.0664
(0.0744)
−0.5773***
(0.0913)
−0.1570
(0.1090)
−0.6456
(1.0814)
Yes
Yes
834
Ln Assets
Ln Firm Age
Profitability
Tangible Assets
R&D Expenditures
Year fixed effects
Firm fixed effects
Number of observations
This table reports OLS estimates; the dependent variable in Columns (1)–(3) is Debt/Capital, whereas in Columns (4)–(6) is Short-Term Debt/Capital. Post is a
dummy equal to one for post-succession years ([0, + 2]) and zero for pre-succession years ([− 3, −1]). Professional Succession is a dummy equal to one if the firm
undertakes a professional succession and zero for a family succession. Controls included, depending on the specification, are logarithm of total assets, profitability,
defined as net income to total assets, fraction of tangible assets, logarithm of firm age, R&D and advertisement expenditures to total assets, year and firm fixed
effects. To save space, coefficients for dummies and intercept are not reported. Robust standard errors are reported in parenthesis. *, ** and *** denote significance
at 10%, 5% and 1% respectively.
1022
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
Table 4
Heterogeneous effects: financial and governance variations.
Dependent variable: Debt/Capital
Post*Professional
Succession
Ln Assets
Young
firms
Old
firms
High
investment
Low
investment
High
presence of
families on
the board
High level
Low
presence of of assets in
families on place
the board
Low level
of assets in
place
High profit
volatility
Low profit
volatility
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.0289**
(0.0132)
0.0424***
(0.0154)
−0.2457
(0.2453)
−0.6780***
(0.1045)
0.2786***
(0.0865)
−0.2763
(0.8637)
Yes
Yes
456
0.0659**
(0.0256)
0.0936***
(0.0263)
0.0616
(0.2019)
−0.9932***
(0.1969)
−0.1182
(0.1062)
−4.4436***
(1.5672)
Yes
Yes
207
0.0395
(0.0301)
0.0682
(0.0505)
−0.3030*
(0.1607)
−0.7949***
(0.2120)
−0.0179
(0.2026)
−0.1966
(0.9350)
Yes
Yes
206
0.1041**
(0.0432)
0.0534***
(0.0143)
0.0804
(0.0757)
−0.6190***
(0.1658)
0.0122
(0.0839)
0.5755
(0.6209)
Yes
Yes
360
0.0474**
(0.0201)
0.0850
(0.0491)
−0.0425
(0.1046)
−0.7088***
(0.1085)
0.0387
(0.1238)
−2.4692
(1.1238)
Yes
Yes
390
0.0435***
(0.0141)
0.0606***
(0.0155)
0.0522
(0.1004)
−0.5633***
(0.0907)
0.0834
(0.0794)
−0.1966
(0.6770)
Yes
Yes
409
0.0408*
(0.0211)
0.0723
(0.0521)
−0.0520
(0.1036)
−0.8536***
(0.1700)
−0.2423
(0.1514)
−0.5226
(0.7599)
Yes
Yes
406
0.0486**
(0.0214)
0.0385
(0.0372)
0.0369
(0.0759)
−0.6526***
(0.1205)
−0.1767*
(0.1055)
−0.3997
(0.5339)
Yes
Yes
371
0.0479***
(0.0181)
0.0845**
(0.0382)
0.0432
(0.1191)
−0.7597***
(0.1709)
0.1494
(0.0986)
−1.2557
(1.8098)
Yes
Yes
365
0.0476**
(0.0223)
0.0736
(0.0534)
Ln Firm Age
−0.0594
(0.1377)
Profitability
−0.6926***
(0.1389)
Tangible Assets
0.2345**
(0.0913)
R&D Expenditures −0.6721
(0.8256)
Year fixed effects Yes
Firm fixed effects Yes
Number of
376
observations
This table reports OLS regressions; the dependent variable is in all Columns Debt/Capital. Young (old) firms are firms below (above) the median firm age. High
(low) investment firms are firms in the upper (lower) quartile of the two-digit industry-adjusted investment distribution post-succession; high (low) presence of
families on the board is formed by firms in which families have a representation above (below) the median family representation on the board of directors postsuccession. Firms with high (low) level of assets in place are firms with a fraction of tangible assets above (below) the median value pre-succession. Lower
(higher) profit volatility represent firms that reduced (increased) the volatility of earnings before interest, taxes, depreciation and amortization to total assets in
the post-succession period, compared to the pre-succession period. Post is a dummy equal to one for post-succession years ([0, + 2]) and zero for pre-succession
years ([− 3, −1]). Professional Succession is a dummy equal to one if the firm undertakes a professional succession and zero for a family succession. Controls
included are logarithm of total assets, profitability, defined as net income to total assets, fraction of tangible assets, logarithm of firm age, R&D and advertisement
expenditures to total assets, year and firm fixed effects. To save space, coefficients for dummies and intercept are not reported. Robust standard errors are reported
in parenthesis. *, ** and *** denote significance at 10%, 5% and 1% respectively.
conflicts between families and professional managers (Maury, 2006; Villalonga and Amit, 2006). In Columns (5) and (6), we split the
sample into firms in which the family has a low (below-median) or high (above-median) presence on the board of directors posttransition. Our results indicate that for the subsample of firms with high presence of families on the board, the effect of professional
successions on debt is more than twice as large as the one obtained on the low presence subsample. This evidence offers weak support
for the view that families use debt to prevent managerial slack (since ownership and board positions already imply strong monitoring
power) and rather suggests that families rely on board positions not only to tightly monitor CEOs but also to assist in decision-making
processes and collaborate in setting a firm's policies. To further test how agency issues influence our findings, we proxy
overinvestment potential by the level of assets in place (Harvey et al., 2004) one year prior to succession, and then we estimate
separate regressions for above- (Column 7) or below-median firms (Column 8). If debt is used as governance device around transition,
the increase should be greater when it is most needed, namely when the overinvestment potential is higher. However, we find that the
impact of professional successions on debt is significant and similar in magnitude in the two subsamples.
Finally, we test whether the change in leverage is a function of the attempt by families to reduce corporate risk in the postsuccession period. Anderson et al. (2010) document that family firms tend to invest in safer projects, such as physical assets. If
professional managers, while improving operating returns, alter families' investment preferences (e.g. by investing in riskier
projects) family owners might influence debt policies to limit corporate risk. We proxy firm risk by the volatility of profits
(measured as the standard deviation of earnings before interest, taxes, depreciation and amortization to total assets), and then we
estimate separate regressions for firms exhibiting an increase (Column 9) or decrease in profit volatility (Column 10) compared to
pre-succession volatility. Our results show that debt changes are positive and significant in both subsamples, thus providing little
support for the view that families adopt more conservative debt choices around transition to mitigate uncertainty induced by e.g.
professional CEOs' investment in riskier projects.
4.3. Financial flexibility and debt changes around transition
We adopt two methods to analyze how debt changes around transition depend on a firm's existing spare debt capacity and
financial flexibility. First, we use a percentile method, splitting the sample into firms with low or high debt one year prior to
succession. In Table 5, Columns (1) and (2), we consider firms below or above the median debt, whereas in Columns (3) and (4)
we compare firms in the lower or upper quartile of the debt distribution. Our results highlight a clear pattern: the increasing effect
of professional successions on debt shows up mainly in firms with low existing leverage at the time of succession; by contrast, in
firms that already have a high leverage, the variation is smaller in magnitude and its statistical significance is lower.
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
1023
Table 5
Debt and financial flexibility.
Dependent variable: Debt/Capital
Leverage prior to succession
Post*Professional Succession
Firm controls
Year fixed effects
Firm fixed effects
Number of observations
Low
High
Lower quartile
(1)
(2)
(3)
0.0612***
(0.0222)
Yes
Yes
Yes
379
0.0200
(0.0166)
Yes
Yes
Yes
381
0.0749**
(0.0361)
Yes
Yes
Yes
187
Spare debt capacity
Liquidity holdings
Yes
No
Low
High
(4)
(5)
(6)
(7)
(8)
−0.0218
(0.0258)
Yes
Yes
Yes
189
0.1044***
(0.0210)
Yes
Yes
Yes
180
0.0265*
(0.0154)
Yes
Yes
Yes
652
0.0607***
(0.0182)
Yes
Yes
Yes
381
0.0267
(0.0177)
Yes
Yes
Yes
379
Upper quartile
This table reports OLS regressions; the dependent variable is in all Columns Debt/Capital. Columns (1) and (2) distinguish between firms with leverage below or above
the median leverage one year prior to succession. Columns (3) and (4) distinguish between firms with leverage in the lower or upper quartile of the leverage distribution
one year prior to succession. Columns (5) and (6) distinguish between firms with or without spare debt capacity. Spare debt capacity is computed by first estimating
with the Arellano–Bond methodology a leverage model which includes as explanatory variables median two-digit industry leverage trends, 1-year lagged leverage, firm
size, fraction of tangible assets, earnings before interest, taxes and depreciation to total assets, year and firm fixed effects. Then, we define as financially flexible those
firms for which the leverage predicted by the model is at least 10% above the observed leverage for at least two years in the period [−3, 0] years before transition.
Columns (7) and (8) distinguish between firms with cash reserves below or above the median value one year prior to succession. Post is a dummy equal to one for postsuccession years ([0, +2]) and zero for pre-succession years ([−3, −1]). Professional Succession is a dummy equal to one if the firm undertakes a professional
succession and zero for a family succession. Controls included are logarithm of total assets, profitability, defined as net income to total assets, fraction of tangible assets,
logarithm of firm age, R&D and advertisement expenditures to total assets, year and firm fixed effects. To save space, coefficients for control variables, dummies and
intercept are not reported. Robust standard errors are reported in parenthesis. *, ** and *** denote significance at 10%, 5% and 1% respectively.
Second, we follow the approach in Mura and Marchica (2010) to identify financial flexibility in the form of spare debt capacity,
measured as the unobserved deviations from a standard leverage model, 11 and then we estimate separate regressions for
financially flexible and inflexible firms. Results, reported in Columns (5) and (6), show that the increase in debt experienced by
firms that have spare debt capacity before succession is significant at 1% level and much larger than the one relative to firms with
limited debt capacity. Overall, these results confirm that pre-succession financial flexibility in the form of unused borrowing
capacity is an important determinant of financial policies associated with the appointment of professional CEOs.
Spare debt capacity is not the only way to create financial flexibility. The evidence in Denis and Sibilkov (2010), and Faulkender
and Wang (2006) indicates that cash holdings are used to enhance investment, especially among financially constrained firms. We
expect that firms with high liquidity holdings at the time of succession have relatively less need to raise debt to finance
professional CEOs' investment policies. The evidence reported in Columns (7) and (8), in which we distinguish between cash-rich
(above-median) and cash-poor firms (below-median) one year prior to succession, is consistent with this argument, although the
difference between the coefficients is slightly smaller than the cases reported in Columns (1)–(6). 12
4.4. Robustness checks
We perform a number of checks to confirm the empirical results reported in Section 4.1. To make sure that results are not
driven by outliers, we trim 1%, 5% or 10% of observations at each tail of the leverage distribution (to save space we only report, in
Table 6, Column 1, results for the 1% trim), or we estimate in Column (2) a median regression (including industry fixed effects and
bootstrapping standard errors through 100 replication).
Frank and Goyal (2009) indicate that industry leverage is one of the factors that reliably influence capital structure decisions. To
absorb this factor, we control for industry trends, computed as the median of each two-digit industry. While industry trends are
positive and highly significant, the increase in debt from professional successions is only marginally smaller and remains
significant at 1% level (Column 3). In Column (4), we include one-year lagged rather than contemporaneous controls. Also, we
assess the robustness to the adoption of different time windows around CEO transitions. In untabulated regressions, we adopt time
windows that exclude the succession year, such as [−3, −1]–[+1, + 2] years or [−2, −1]–[+1, + 2] years around transition, or
include the succession year in the pre-succession period, such as [− 2, 0]–[+1, + 2] years around transition. The debt increase
remains positive and significant at conventional levels for all the alternative time windows considered.
11
The procedure consists of several steps. First, we estimate a leverage model using as explanatory variables median two-digit industry leverage trends, one-year
lagged leverage, firm size, fraction of tangible assets, earnings before interest, taxes and depreciation to total assets, year and firm fixed effects. The model is estimated
using the Arellano–Bond methodology to avoid inconsistency problems due to the correlation between lagged leverage and differentiated residuals. Then, we define as
financially flexible those firms for which the leverage predicted by the model is at least 10% above the observed leverage for at least two years in the period [−3, 0] years
before transition. Our results are robust to the adoption of an extended leverage model which also includes cash holdings and debt maturity as explanatory variables.
Also, we obtain similar findings if we adopt a less restrictive deviation threshold, e.g. 5%, instead of 10% which is used as baseline threshold.
12
In additional analyses, we find that the effects reported in Table 5 are, not only economically, but also statistically different from each other. In particular, coefficients
reported in Columns (1) and (2) are statistically different at 10% level; coefficients reported in Columns (3) and (4) are statistically different at 5% level; coefficients
reported in Columns (5) and (6) are statistically different at 1% level; coefficients reported in Columns (7) and (8) are statistically different only at 12% level.
1024
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
Table 6
Robustness checks.
Dependent variable: Debt/Capital
Post*Professional
Succession
Firm controls
Year fixed effects
Firm fixed effects
Number of observations
Trimming
1% of obs.
Median
regression
Including
industry
trends
Lagged
controls
Clustered
standard
errors
Excluding
groups and
listed firms
Family
control and
involvement
Excluding firms
with multiple
CEOs
Central
departing
ages
Atypical
departing
ages
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.0402***
(0.0120)
Yes
Yes
Yes
815
0.0312*
(0.0181)
Yes
Yes
No
832
0.0394***
(0.0130)
Yes
Yes
Yes
832
0.0452***
(0.0157)
Yes
Yes
Yes
649
0.0431**
(0.0210)
Yes
Yes
Yes
832
0.0604***
(0.0142)
Yes
Yes
Yes
707
0.0402***
(0.0136)
Yes
Yes
Yes
635
0.0355**
(0.0149)
Yes
Yes
Yes
655
0.0342*
(0.0200)
Yes
Yes
Yes
298
0.0309*
(0.0185)
Yes
Yes
Yes
351
This table reports OLS regressions, except for Column (2) which reports a median regression including industry fixed effects instead of firm fixed effects and
computing standard errors by bootstrap, using 100 replications. The dependent variable is in all Columns Debt/Capital. In Columns (1), we trim 1% of observations
on the left and right tails of the leverage distribution; In Column (3), we include as additional control the two-digit industry trends; in Column (4), we estimate the
model using one-year lagged controls; in Column (5), we cluster standard errors by firm; in Column (6), we exclude firms that belong to business groups and listed
firms. Column (7) contains a subsample of family-controlled firms in which at least two family members hold a position as owners and/or board members at the
year of succession. In Column (8), we exclude firms with multiple CEOs in the post-succession period. In Columns (9) and (10), we use two subsamples based on
the age of the departing CEO (when available); Column (9) contains age departures between 60 and 75 years, whereas Column (10) contains age departures
younger than 60 or older than 75 years. Post is a dummy equal to one for post-succession years ([0, + 2]) and zero for pre-succession years ([− 3, −1]).
Professional Succession is a dummy equal to one if the firm undertakes a professional succession and zero for a family succession. Controls included are logarithm
of total assets, profitability, defined as net income to total assets, fraction of tangible assets, logarithm of firm age, R&D and advertisement expenditures to total
assets, year and firm (or industry, in Column 2) fixed effects. To save space, coefficients for control variables, dummies and intercept are not reported. Robust
standard errors are reported in parenthesis. *, ** and *** denote significance at 10%, 5% and 1% respectively.
To deal with the autocorrelation in residuals, we cluster standard error at the firm level (Column 5), or we follow Bertrand et al.
(2004) and collapse our data into two periods, before and after the succession (result unreported). In both cases, the impact of
professional successions on debt remains statistically significant at 5% level.
As mentioned in Section 2, our sample contains a vast majority of private stand-alone firms, but also a few firms that are listed
and/or affiliated with business groups. Because capital structure decisions of listed firms and business groups are typically
different than the ones of private companies (e.g. because of lower financial constraints or access to internal capital markets), we
restrict the sample to private stand-alone firms. Column (6) shows that the debt increase is slightly larger than the average effect
and remains significant at 1% level. In unreported analyses, we also confirm that our findings are unchanged if we exclude the few
firms in financial and insurance sectors.
In Column (7), we deal with the difficulty in defining family firms (see Miller et al., 2007 for a discussion) by adopting a more
restrictive definition based on both family control and family involvement. In addition to the majority of equity stakes owned by a
family, we require the presence of at least two family members as owners and/or board members. Confining the analysis to this
subsample does not alter the economic and statistical significance of our results. Finally, we exclude the few firms having multiple
CEOs in the post-succession period since in these cases it is not clear how the decisional power is allocated among them, and
estimating the effect of one CEO succession only on corporate policies may suffer from biases. The estimate reported in Column (8)
is both statistically and economically in line with our baseline results.
4.5. Endogeneity issues
As discussed in previous works (see e.g. Bennedsen et al., 2007; Cucculelli and Micucci, 2008), successions are likely to
incorporate unobservable factors, reflect on a firm's past outcomes or future prospects, thus inducing endogeneity concerns in the
timing of successions. If family members inherit management positions when the company is “in good shape” (e.g. because, as
suggested by Adams et al. (2009), the founder delays the succession until the performance has reached given levels), then the
differences in financial policies between professional and family successions may merely reflect past firm conditions rather than
distinct post-succession strategies.
We mitigate this concern in two ways. First, we examine the robustness on alternative subsamples based on the departing
CEOs' age (Bennedsen et al., 2007). In particular, we perform separate regressions for CEO retirements at the age interval between
60 and 75 years, and retirements outside this interval. As indicated in Table 6, Columns (9) and (10), both coefficients are
significant at conventional levels and close to the effect estimated on the entire sample. We obtain similar results (untabulated) by
performing separate regressions for young (below-median age) and old (above-median age) departing CEOs. Second, we apply a
matching procedure as in Cucculelli and Micucci (2008). Instead of employing firms that undertake family successions as a control
group, which may bias the results due to the above-mentioned concerns, this methodology allows us to construct an alternative
control group based on family firms that do not undertake a succession but closely resemble firms that undertake a professional
succession. The matching procedure is performed on pre-succession return on assets, debt to capital ratio, regional localization,
two-digit industry and year dummies. We adopt a nearest-neighbor propensity-score matching without replacement and
discarding the few observations outside the common support, which ensures that for each family firm choosing a professional
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
1025
Table 7
Impact of CEO successions on debt: matched control group.
Dependent variable:
Debt/Capital
Post*Professional Succession
Firm controls
Year fixed effects
Firm fixed effects
Number of observations
Short-Term Debt/Capital
(1)
(2)
(3)
0.0318**
(0.0156)
No
Yes
Yes
657
0.0422***
(0.0157)
Yes
Yes
Yes
605
0.0328*
(0.0178)
Yes
Yes
Yes
607
This table reports OLS regressions; the dependent variable in Columns (1)–(2) is Debt/Capital; in Column (3) is Short-Term Debt/Capital. Post is a dummy equal to
one for post-succession years ([0, + 2]) and zero for pre-succession years ([− 3, −1]). Professional Succession is a dummy equal to one if the firm undertakes a
professional succession and zero for family firms not undertaking a CEO succession but representing the nearest match on the basis of pre-succession firm
characteristics. Variables matched are regional localization, two-digit industry, debt to capital ratio and return on assets one year prior to succession. We use a
nearest-neighbor propensity-score matching without replacement and we restrict observations within the common support. Controls included, depending on the
specification, are logarithm of total assets, profitability, defined as net income to total assets, fraction of tangible assets, logarithm of firm age, R&D and
advertisement expenditures to total assets, year and firm fixed effects. To save space, coefficients for control variables, dummies and intercept are not reported.
Robust standard errors are reported in parenthesis. *, ** and *** denote significance at 10%, 5% and 1% respectively.
succession we can identity a non-succession firm having the closest observable characteristics prior to succession. Table 7 reports
the DD estimates using such matched firms as alternative control group, and including year and firm fixed effects to absorb
unobserved firm heterogeneity and time effects. Results indicate that our findings remain unchanged in both economic and
statistical terms.
4.6. Alternative dependent variables
In Table 8, we analyze the impact of professional successions on alternative corporate outcomes. We prove the robustness of
our findings to the adoption of alternative leverage ratios. First, we employ the ratio of total liabilities to assets; second, we focus
on bank debt. Results, reported in Columns (1) and (2), confirm that professional successions cause a significant increase in debt.
Sraer and Thesmar (2007) find that family firms managed by professional CEOs pay lower interest rate on debt, although there are
no significant differences between founder/heir-managed firms and non-family firms. Starting from this finding, we test whether any
difference emerges in the cost of debt between family and professional successions around transition. We employ the ratio of interest
paid to financial debt as the dependent variable to test whether professional managers are able to find cheaper sources of debt
financing after transition. Although this ratio is not a precise measure of the cost of debt, Sraer and Thesmar (2007) argue that it should
approximately represent the average of all spreads on all loans and bonds, weighted by the sizes of the various issues. Our evidence,
reported in Column (3), indicates that professional and family successions do not have a different impact on the cost of debt.
In Column (4), we use industry-adjusted investment as the dependent variable. Our estimates indicate that professional
successions cause a significant increase in investment. In untabulated analyses, we find that such increase is larger for firms with
spare debt capacity and financially flexible firms before succession. For example, for the subsample of firms with spare debt
capacity, as classified in Table 5, Column (1), the increase in investment is 0.0884 (significant at 1% level). This evidence supports
the idea that pre-succession financial flexibility enhances incoming professional CEOs' investment ability. Finally, by using return
on capital employed (ROCE) as dependent variable (Column 5), we confirm existing evidence (see e.g. Bennedsen et al. 2007;
Table 8
Impact of CEO successions on alternative outcomes.
Dependent variable:
Post*Professional Succession
Firm controls
Year fixed effects
Firm fixed effects
Number of observations
Liabilities/Assets
Bank Debt/Capital
Interest paid on debt
Industry-Adjusted Investment
ROCE
(1)
(2)
(3)
(4)
(5)
0.0394***
(0.0124)
Yes
Yes
Yes
832
0.0313**
(0.0146)
Yes
Yes
Yes
724
0.7144
(0.5005)
Yes
Yes
Yes
639
0.0499**
(0.0196)
Yes
Yes
Yes
779
0.0472*
(0.0281)
Yes
Yes
Yes
884
This table reports OLS regressions; the dependent variable in Column (1) is the ratio of total liabilities to total assets; in Column (2) is the ratio of bank debt to
capital, for firms that report non-zero values; in Column (3) is the interest rate paid on debt, when available, expressed in %; in Column (4) is industry-adjusted
investment, defined as the sum of capital expenditures, R&D and advertisement expenditures at time t divided by start-of-year total assets, minus the two-digit
industry median computed using all family firms; in Column (5) is the return on capital employed (ROCE), computed as net income divided by the sum of book
value of debt and equity. Post is a dummy equal to one for post-succession years ([0, + 2]) and zero for pre-succession years ([− 3, −1]). Professional Succession is
a dummy equal to one if the firm undertakes a professional succession and zero for a family succession. In Columns (1)–(3), we control for logarithm of total assets,
profitability, defined as net income to total assets, fraction of tangible assets, logarithm of firm age, R&D and advertisement expenditures to total assets. In Columns
(4) and (5), we control for logarithm of firm age. Year and firm fixed effects are included in all Columns. To save space, coefficients for control variables, dummies
and intercept are not reported. Robust standard errors are reported in parenthesis. *, ** and *** denote significance at 10%, 5% and 1% respectively.
1026
M.D. Amore et al. / Journal of Corporate Finance 17 (2011) 1016–1027
Cucculelli and Micucci 2008) that professional CEOs invest in value-enhancing projects and are able to improve a firm's operating
returns.
5. Concluding remarks
Our study examines the consequences of blood-unrelated professional CEOs and financial flexibility for the financial policies of
Italian family-controlled firms. Our estimates indicate that appointing CEOs from outside the controlling family causes an
economically and statistically significant increase in debt financing around transition, primarily driven by short-term maturities.
We document substantial heterogeneity in the impact of professional successions on debt; the increase in debt is greater when the
firm is younger and fast-growing, when the family has an active influence on managerial decision-making through the board of
directors, and when the incoming CEO has limited access to alternative funding sources. Furthermore, our analysis indicates that
financial flexibility plays a decisive role around transition: the increase in debt induced by professional successions occurs
primarily when the incoming manager can exploit spare borrowing capacity. We interpret these findings as consistent with the
view that debt is used to meet funding needs to sustain professional CEOs' investment ability without incurring control dilution
that equity issuances would imply. The positive association between debt increases and investment around professional
transitions supports this interpretation.
Taken together, our results support the notion that professional and family CEOs, by having distinct managerial skills and
business relationships with the controlling family, provide a very different service to the companies they manage. Our analysis also
contains valuable managerial implications and complements existing family business literature describing CEO successions in a
process perspective (Le Breton-Miller et al., 2004; Miller et al., 2003; Sharma et al., 2003). While the existing literature portrays
succession as a process in which shared long-term vision and selection of successors are the most crucial elements (Miller et al.,
2003), we identify financial policies and borrowing capacity as factors that influence incoming professional CEOs' ability to
improve firm prospects. A growth-oriented succession planning should be accompanied by capital structure adjustments in order
to ensure enough financial flexibility to incoming professional managers and, at the same time, to secure firm survival.
Acknowledgments
The data used in this paper were collected during the 1st Edition of the “AUB (AIdAF-Unicredit-Bocconi University) Italian
Observatory on Family Firms”. The AUB Observatory has been promoted by AIdAF-Associazione Italiana delle Aziende Familiari
(Italian Association of Family Firms), realized under the scientific supervision of researchers at the AIdAF-Alberto Falck Chair of
Strategic Management in Family Business — Bocconi University, in collaboration with Unicredit Corporate and Private Banking,
which we thank for financial support. We also thank Morten Bennedsen and Kurt Desender for useful discussions, Jeffry Netter
(the Editor) and an anonymous reviewer for many insightful comments that significantly improved the paper. Amore is grateful
for hospitality from INSEAD. All errors remain our own.
Appendix
Panel A. Data sources
Ownership and management data (2003–2007)
Accounting data (2000–2009)
Panel B. Structure of the entire dataset
Firms in AIDA with turnover above Eur50 million (as of 2007)
Firms in which a family holds the majority of equity stakes
Of which stand-alone firms, holding company for mono-business groups, controlled companies for diversified groups, and excluding
duplications
Other ownership structures (including widely-held firms, state-owned firms, firms owned by foreign investors, firms owned by
coalitions, cooperatives, and firms without available ownership data)
Italian Chamber of
Commerce
AIDA — Bureau Van Dijk
7663
4251
2484
3412
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