Does CEOs’ familiarity with business segments affect their
divestment decisions?*
James Ang
College of Business
FloridaStateUniversity
e-mail: jang@cob.fsu.edu
Abe de Jong+
Rotterdam School of Management
Erasmus University
e-mail: ajong@rsm.nl
Marieke van der Poel
Rotterdam School of Management
Erasmus University
e-mail: mpoel@rsm.nl
March 2013
*
We are grateful to Malcolm Baker, Guillermo Baquero, John Doukas, Marie Dutordoir, Denys Glushkov, Nancy
Huyghebaert, Ulrike Malmendier, Gerard Mertens, Peter Roosenboom, Len Rosenthal, Frederik Schlingemann, Jack
Stecher, Jeroen Suijs, Mathijs van Dijk, an anonymous reviewer, participants at the 2005 ERIM Conference
Financial Management Track, participants at the 2006 EFMA conference (Madrid), participants at the 2007 EFA
conference (New Orleans), participants at the 2007 EFA conference (Ljubljana), participants at the 2007 FMA
conference (Orlando), seminar participants at RSM Erasmus University, at the University of Antwerp, at the
Norwegian School of Economics and Business Administration, at the Catholic University of Leuven, at Florida State
University, and at Tilburg University for providing helpful comments and suggestions. We are also grateful to the
Vereniging Trust Fonds for providing financial support and to Sandra Sizer for excellent editing.
+
Corresponding author:Department of Finance (Room T09-55), RSM Erasmus University, PO Box 1738, 3000 DR
Rotterdam, The Netherlands; Phone: +31 10 408 1022; Fax: +31 10 408 9017
1
Does CEOs’ familiarity with business segments affect their
divestment decisions?
Abstract
We examine the impact of CEOs’ familiarity with business segments on their divestiture decisions. We
find that CEOs are less likely to divest assets from familiar segments, when compared to non-familiar
segments. Our evidence suggests that CEOs favor their familiar segments, because they are more
informed about these segments relative to non-familiar segments.It does not support CEO selection and
managerial entrenchment as main explanations forthe familiarity effect.Even though divestitures from
familiar segments are least likely to occur, these divestitures generate the highest abnormal announcement
returns.
Keywords: Divestitures; Familiarity; Managerial entrenchment; CEO selection
2
1. Introduction
Divestment decisions in corporations representmajor restructurings of the business. In this paper, we
examine whether divestment decisions by CEOs are affected by their career paths. Recent research in
corporate finance has emphasized that managers’ characteristics play an important role in their
investment, financial and strategic decisions (e.g., Bertrand and Schoar (2003), Malmendier and Tate
(2005, 2008),Billet and Qian (2008), andChang, Dasgupta, and Hilary (2010)). In addition, substantial
empirical evidence shows that investors and managers tend to have preferences for choosing the familiar.
For instance, investors tend to invest in familiar companies, ignoring optimal portfolio principles (e.g.,
French and Poterba (1991), Huberman (2001), Li (2004),Parwada (2008), and Graham, Harvey, and
Huang (2009)). Firms show a similar geographic proximity effect in their cross-listing decisions
(Sarkissian and Schill (2004)) and banking choices (Berger, Dai, Ongena, and Smith (2003)).
We inquire whether the likelihood to divest assets differs between segments that are familiar to
the CEO and other, non-familiar segments. CEOs’ familiarity with segments comes from their career
path, i.e., prior work experience in these or related segments. We analyze business segments of multisegment firms that announce a divestment for a sample of 608 multi-segment firm years, comprising 768
segments with divestments, and 1233 segments that are fully retained. We find that CEO familiarity is
associated with a reluctance to divest assets;CEOs divest 35% less often in segments in which they
worked prior to being promoted as CEO(also referred to as the CEO’s direct-experience segment). The
familiarity effect holds for different transaction sizes.
After documenting the familiarity effect on divestment decisions, we examine three non-mutually
exclusive explanations. Our first explanation is that boards specifically select CEOs with work experience
in a particular business segment to grow and focus on this segment as a part of the firm’s strategy.We test
two predictions of this CEO selection explanation. First, if the CEO’sdirect-experience segment was
desirable to be fully retained, the segment should have a lower ex ante probability to divest assetsrelative
to the firm’s other business segmentsin the year prior to the CEO’s appointment. Second, the effects of a
mandate from the board to the new CEO to grow the direct-experience segment should be particularly
strong in the first years of the CEO’s tenure. Our empirical evidence does not lend support to either
prediction. We find that direct-experience segments have a higher rather than a lower ex ante probability
to divest assets. In addition, our findings indicate that, instead offavoring familiar segments at the
beginning of their tenure, CEOs favor familiar segments and divest assets from non-familiar segments
later in their tenure.
Our second explanation for the familiarity effect is CEO entrenchment (Shleifer and Vishny
(1989)). To increase the proportion of assets that are complementary to their own skills, CEOs would
divest assets from non-familiar segments. This divestment strategy to favor familiar segments facilitates
3
CEOentrenchment by making them less dispensable. As effective governance mechanisms should reduce
managerial entrenchment, we hypothesize but fail to find a significant impact of governance mechanisms
on the familiarity effect and therefore cannot support the entrenchment hypothesis.
Our third explanation is that CEOs are more informed about familiar segments than about nonfamiliar segments. We approach this explanation from the perspective of an internal capital market where
we view divestitures as a negative capital allocation. In allocating capital to segments, CEOs account for
information asymmetry between them and segment managers by scaling down the segments’ allocated
share of the budget (Harris and Raviv (1996, 1998); Bernando, Cai, and Luo (2001)).We argue that CEOs
are more likely to divest assets from non-familiar segments, because the information asymmetry is greater
between CEOs and managers of non-familiar segments than between CEOs and managers of familiar
segments. Our evidence is in line with the information explanation. We distinguish between direct work
experience, industry work experience and no work experience in a segment and find that CEOs are least
likely to divest assets from segments of which CEOs have most detailed information, which is of their
direct-experience segment.
We further investigate the role of information and explore a particular version of CEO familiarity
with their direct-experience segment that relates to their relationship with existing personnel in the
business segment. The CEO may be reluctant to sell off parts of segments with persons that previously
worked under them out of a sense of loyalty and friendship. We postulate that CEOs’ information and the
strength of their personal relations should fade over time as these particular persons would retire, resign,
or reassign over time, and thus, the CEOs’ partiality to protect the personnel in the segment from
divestments would decline with the time since they left the segment. We do not find the predicted
diminishing familiarity effect.Instead, CEOs still exhibit the familiarity effect even after they left their
direct-experience segment over nine years ago. The persistent familiarity effect suggests that, because the
CEOs accumulated years of knowledge concerning their direct-experience segment, and having scaled the
learning curve, they would find, after becoming CEO, the costs to keep up with this segment and its
respective industry relatively easier than with segments in which they have had no previous experience.
In our final analyses, we conduct an event study around divestiture announcements. Consistent
with CEOs’ superior information on direct-experience segments, we show that they generate higher
abnormal returns with divestitures from these segments. Remarkably, the greater returns are particularly
pronounced among divestitures that CEOs are least likely to make, i.e.,direct-experience divestitures of
longer-tenured CEOs. The difference in abnormal returns amounts to 1.6%.
The main contribution of this paper is that we demonstrate significant effects of the professional
career paths of CEOs on corporate strategy and value. Even though we find that CEOs are predicted to
divest assets from their direct-experience segments at the time they are hired and that direct-
4
experiencedivestitures generate the highest returns, the variation in CEOs’ information sets among
different segments decreases the likelihood to divest from their direct-experience segment.
We consider alternative explanations for the documented familiarity effect and perform several
tests to establish the robustness of our results. For instance, a CEO’s direct experience in a segment could
be a proxy for the segment being core and hence our documented familiarity effect may be a mere
reflection of a firm’s decision to focus on their core business. However, we find no evidence of a
mechanical relation. We distinguish between core segments, which are the largest segments that operate
in the same industry as the primary industry of the firm, and non-core segments, andshow that CEOs tend
to be particularly reluctant to divest assets from the non-core direct-experience segments.
Our paper contributes to recent studies on executives’ career paths that distinguish between
generalist and specialist CEOs. For instance, Murphy and Zabojnik (2004) describe the general
management skills have become more important for CEO pay, when compared to firm-specific
experience and skills. Because bargaining power in the labor market may explain this effect, Custódio,
Ferreira and Matos (forthcoming) investigate the effects of the composition of general skills of CEOs.
They find that CEOs have become more generalist and that a premium in pay is provided, particular
during periods of restructurings and acquisitions. A CEO who is familiar with a particular segment can
both be a specialist and a generalist, leading to the interesting question whether our result is stronger for
either type of CEO. We study this question for divestiture decisions and find there is no difference in the
familiarity effect for generalist versus specialist CEOs.
Our paperadds to the stream of literature about capital allocation decisions (see Stein (2003) and
Maksimovic and Philips (2007) for a review of the literature). The study that has the most direct
connection to ours is Xuan (2009), who shows that newly-hired CEOsincrease investments in nonfamiliar segments. He argues that these CEOs find it expedient to increase investments in non-familiar
segments to induce cooperation with managers of those segments. When divestitures are viewed as a
negative capital budgeting problem, our analysis is a mirror image of Xuan (2009) and yields parallel
results in that new CEOs also find it expedient to favor non-familiar segments in the first two years of
their tenure. However, we extend our analysis beyond two years of CEO tenure and find that CEOs show
their true preference for familiar segments when they become more established.
Our paper also contributes to the divestment literature. One related paper is that of Huang (2010),
who shows that, in order to create a better match between the CEO and his firm, CEOs divest complete
segments that operate in industries in which they do not have work experience, which improves the firm’s
performance. Our paper includes both complete and partial divestments of segment, which allows us to
examine CEOs’ restructuring decisions within segments. We show that divestitures from the CEO’s
direct-experience segments generate the highest abnormal returns, suggesting that CEOs are able to pick
5
winners and discard losers (as in Stein (1997)) within their direct-experience segments, which is a
consequence of their deeper knowledge of the segment. A complementary paper to ours is Landier, Nair,
and Wulf (2009), who study the effects of geographic location on corporate decisions. Interestingly, in
their paper proximity of a segment to its headquarters is geographical distance (i.e., whether a segment
and headquarter are in the same state), while our study measures social distance, i.e. the CEOs’ personal
ties with segments. The authors show that CEOs are less likely to divest divisions and lay off employees
of divisions that are more proximate to their headquarters and that information constraints explain why
CEOs favor more proximate employees, which complements our information explanation.
The paper is organized as follows. Section 2 documents the familiarity effect. In Section 3 we
investigate three explanations for the familiarity effect. In Section 4 we describe the valuation effects of
familiarity. Section 5 concludes.
2. Does CEO familiarity affect divestiture decisions?
2.1. Sample and data sources
We construct our initial sample from the Compustat Business Information File and the Securities Data
Corporation (SDC) file. We select data for firms with at least two business (or operating) segments for
our sample period, 1996-2004. As in Schlingemann, Stulz, and Walkling (2002), we select firms with
sales of over $20 million or assets above $100 million, and we exclude American Depository Receipts
(ADRs) and firms that are not incorporated in the U.S. We also omit firm years with segments that
operate in regulated industries (SIC 4900 – 4999). Like Berger and Ofek (1995) and Schlingemann, Stulz,
and Walkling, we require that the sum of segment sales does not deviate more than 1% from total firm
sales. These selection criteria result in a sample of 5,251 firm years for 1,009 firms for our sample period.
Next, we use the SDC database for all completed divestments for the 1996-2004 period made by
these 1,009 multi-segment firms, providing us a data set comprising 1,317 firm years (530 firms) with
divestitures. Similar to Xuan (2009, p.4921), we measure divestitures at the level of segments and use the
terms segment and division interchangeably. It should be noted that segments or divisions may consist of
several business units and the firm can divest a full division or one or more business units. Taken from
SDC provided information, we require that more than 95% of the unit’s assets be acquired by the buying
firm after the transaction (as in McNeil and Moore (2005)).We manually link the divested business units
with the business segments reported in Compustat by using the SDC synopsis on the divestiture, the SDC
SIC codes, and the SDC business description of the divested assets. In addition, we search the SEC 10-K
filings for descriptions of segments and discontinued operations. Since the unit of observation of our main
6
analysis is a segment in a given year, we treat multiple divestitures within one segment year as a single
segment observation.1
We require segments to have at least two years of data prior to the divestment. During our sample
period, which includes the introduction of SFAS 131 in 1997, several firms change their segment
reporting (see Berger and Hann (2003, 2007)). 2 Compustat provides revised historical financial
information for the new segments for the two years prior to the new segment reporting, based on firms’
annual reports. We exclude 589 firm years from 192 firms due to incomplete historical Compustat data.
For our familiarity variables we require detailed information about CEOs’ working experience
inside and outside their current firm. We start with the Marquis Who’s Who database and Hoover’s
database. These sources provide summaries of top executives’ previous positions. In the case we cannot
reconstruct the CEO’s career from these sources, we check details in the SEC 10-K and proxy filings.
When these sources also lack sufficient data to reconstruct a CEO’s work experience, we exclude the
observation (in total, we exclude 49 firm years for 21 firms).We would like to add that these sources are
imperfect and we may miss an executive’s prior experience because of incomplete reporting. However, in
our view, this would lead to a bias against finding economically and statistically significant effects for
variables based on experience. We do not further distinguish the levels of previous positions.
We furtherexclude 71 firm years (15 firms) in case the firm has two different CEOs, the
classification of a divestiture is ambiguous, or the divestiture occurs in a corporate segment where firms
divest assets acquired from a merger in the previous year. This selection procedure provides us 608 firm
years with 2,394 segment years from whichwe exclude 393 segment years with incomplete corporate
financial information or that are tagged as “elimination” (as in Lamont (1997)), resulting in 2,001
segment years. Of these 2,001 segment years, 768 are divesting segment years that in total divest 1,032
business units.
2.2. Familiarity measure
We construct three proxy variables for familiarity at the segment level that differentiate among the three
levels of CEOs’ relevant work experience prior to being appointed as CEO. Because understanding our
measures is crucial for our analysis, we illustrate the definitions with an example.In the fiscal year 1998,
1
We note that if multiple divestitures occur in different segments, we classify more than one segment within one
firm year as divesting segment.
2
We note that under SFAS 131, firms do not always report segments based on industry, but can also report their
segments based on vertical integration. We believe that this change in segment reporting does not affect our
conclusions. If anything, it would add noise to our analyses; hence work against finding significant results.
7
Bausch & Lomb disclosed four segments: Vision Care (two-digit SIC 28 and 38), Eyewear (two-digit SIC
38), Pharmaceuticals (two-digit SIC 28), and Healthcare (two-digit SIC 2 and 28). In 1998 the CEO of
Bausch & Lomb Inc was William Carpenter, who was previously employed as a global business manager
in the Bausch & Lomb’s Eyewear segment from March 1995 until December 1995. Before joining
Bausch & Lomb in 1995, he had various positions at Johnson & Johnson in the period starting from
1977until 1991 and was president and CEO of Reckitt & Coleman from 1991 until 1995.
Our first measure, which is the strongest form of familiarity, is direct work experience within a
segment. Because Carpenter worked in the Bausch & Lomb’sEyewear segment as a global business
manager, we classify this segment as the segment with which the CEO has direct work experience. With
this form of familiarity, a CEO has knowledge of the segment’s industry and is informed about the
segment’s internal operations. He also had direct personal relations with its personnel and management.
The second level of familiarity is inside-industry work experience in a related segment. With this
level of familiarity, although CEOs did not directly gain work experience in the reported segment itself,
they gained experience in the same industry as the reported segment. We specifically refer to insideindustry experience, because the CEO gained the industry experience within his firm by working for a
segment that operated in the same two-digit SIC industry as the reported segment.3 In our example, the
Vision Care segment operates in the same two-digit industry (SIC 38) as the Eyewear segment for which
Carpenter worked as global business manager, which gives him inside-industry experience in the related
Vision Care segment.
The third level of familiarity is outside-industry work experience in a segment. With this level of
familiarity, CEOs gained industry experience in the same two-digit SIC industry in other firm(s) as the
reported segment before joining their present firm. To classify a segment as a segment in which the CEO
has outside-industry work experience, we first identify manually the SIC codes of all firms for which the
CEO had worked prior to joining the current firm and then relate those SIC codes to the SIC codes of the
reported segments. In our example, one of Carpenter’s former employers, Johnson & Johnson, operates in
the two-digit SIC industries of all segments of Bausch & Lomb, making Carpenter familiar with all
segments, based on his outside-industry experience. Carpenter’s other former employer, Reckitt
&Coleman, does not operate in any of the segment’s industries.
Please note that the latter two proxies represent industry knowledge about the segment,
suggesting that CEOs are less likely to have personal connections in these segments and will have less
specific knowledge on assets, procedures, and developments within these segments compared to segments
3
Throughout the paper we use two-digit SIC industry codes. We conducted robustness analyses with three-digit SIC
codes and find qualitatively similar results.
8
in which they have direct work experience. Therefore, we maintain that direct work experience is the
strongest form of familiarity, followed by inside-industry experience and outside-industry experience.
The weakest form of familiarity is having no direct or industry work experience with a segment
(“unfamiliar segment”). We treat the classification of the CEO’s familiarity with each segment as
mutually exclusive, where the segments are assigned the strongest form of familiarity. In our example,
that means that we classify the Eyewear segment as a direct work experience segment, the Vision Care
segment as inside-industry work experience segment and both Pharmaceuticals and Healthcare segments
as outside-industry work experience segments. Carpenter has no segments he is unfamiliar with.
2.3. Control variables
To examine the type of segment that is selected for divestiture, we include several control variables. For
each business segment we obtain information on sales, assets, net capital expenditures (calculated as gross
capital expenditures minus depreciation and amortization), cash flows (which we calculate as operating
profit plus depreciation and amortization), and primary and secondary SIC codes from the Compustat
Business Information File.4 We obtain financial firm-level variables; variables to calculate the segments’
Tobin’s q, segment industry-adjusted measures, and the firm’s primary SIC code from the Annual
Compustat File. We obtain governance information from the IRRC and share price information from
CRSP.
We classify a segment as a core segment if the primary two-digit SIC code of the segment
corresponds with the primary two-digit SIC code of the firm and the segment is the largest segment in the
firm in terms of reported segment sales. To facilitate comparability, we apply the same method as
Schlingemann, Stulz, and Walkling (2002) for the industry measures. We calculate these measures as the
median of all Compustat firms with the same two-digit SIC code in the fiscal year prior to the divestiture
announcement. For more reliable industry measures, we require that at least five firms operate in the same
industry. The Tobin’s q of a segment is the industry ratio of the market value of assets to the book value
of assets, for which we use similar data items as Malmendier and Tate (2005, 2008).5 We use item 12 for
the calculation of median industry sales, item 13 for median industry cash flows, and items 128 and 14 for
median industry net capital expenditures. As in Ahn and Denis (2004), we estimate cross-subsidization as
the segment’s industry-adjusted investment minus the firm’s sales weighted sum of industry-adjusted
4
We follow the recommendation of Kahle and Walkling (1996) to use Compustat’s industry classification and not
that of CRSP and to use the historical industry classifications throughout our analyses.
5
We calculate the market value of assets as book value of total assets (item 6) plus market equity minus book
equity.The market equity=(item 25 * item 199); the book equity=(item 216 - item 10 + item 35 - item 336).
9
investment. We follow Schlingemann, Stulz, and Walkling(2002) in calculating segment liquidity: we
divide the total value of acquisition transactions by the total assets in that industry, excluding values
higher than one and industries with less than ten firms from the sample.
At the firm level, we calculate leverage as total debt (item 181) divided by total assets (item
6).6The firm’s Tobin’s q is the market to book value of assets, for which we use the same data items as
the Tobin’s q of a segment. We calculate net capital expenditures as gross capital expenditures (item 128)
minus depreciation and amortization (item 14). We use item 1 for the firm’s cash and short-term
equivalents.
We follow Berger and Ofek (1995) in calculating excess value, which is the percentage difference
between a firm’s total value and the sum of imputed values of its segments as stand-alone firms.We
define excess value as equal to ln(V/I(V)), where V is the total firm value calculated as the market value
of equity (item 199 * item 25) plus book value of debt (item 181) and I(V) the imputed value of the sum
of a firm’s segments as stand-alone firm.
I (V )  i 1 AI i * ( Ind i (V / AI ) mf ) ,
n
(1)
whereAIi is segment i’s sales, n the number of segments and Indi(V/AI)mf the multiple of total capital to
sales for the median single-segment firm with at least $20 million sales in segment i’s industry. We
follow Berger and Ofek (1995) and Schlingemann, Stulz, and Walkling (2002) by basing the industry
median ratios on the narrowest SIC grouping with at least five firms within that industry, and by
excluding from our sample and considering as outliers any values larger than 1.386 or smaller than 1.386.
To calculate the firm’s diversity in q, we follow Rajan, Servaes, and Zingales (2000) and
Schlingemann, Stulz, and Walkling (2002):
Diversity 

n
i 1
( Sales i / i 1 Sales i ) (qi  q ) 2 / q ,
n
(2)
where Salesi refers to segment i’s sales, n to the number of segments, qi to the median q of all Compustat
firms with the same two-digit SIC code as segment i, and q to the sales-weighted average imputed q
across the n segments of the firm.
6
We note that our sample contains 15 observations in which the firm’s leverage exceeds one, which is theoretically
not possible. Therefore, we set these values to one. Not setting these observations to one does not influence our
results.
10
We use the entrenchment index constructed by Bebchuk, Cohen, and Ferrell (2009), which is a
score for anti-shareholder charter provisions. The percentage of independent directors is the number of
independent directors divided by the total number of directors.
We estimate the abnormal returns to divestiture announcements using the market model as
described by MacKinlay (1997). Our estimation window runs from day -160 to -41 relative to the
announcement date. We aggregate the abnormal returns over the day prior to the divestiture
announcement until the day after the divestiture announcement.
2.4. Summary statistics
Table 1 provides the statistics for our sample of divesting firm years.
– Please insert Table 1 here –
We first show the firm statistics. The sample firms have average sales of over $7 billion and average
assets of over $9 billion. The leverage level is relatively high (64%), which may result from the
selectionof divesting firm years. Our average sample firm performs well, with positive cash flows prior to
the divestment year and an average Tobin’s qof 1.665. The average number of segments in which the
firms operate is 3.291 and the average diversity in q across segments equals 0.104. Our sample of
divesting firm years further shows a positive average excess value of 0.004, indicating that the average
firm in our sample does not underperform its single-segment counterparts. The entrenchment index ranges
from zero to five, with an average of 2.436. On average, the firms in our sample have 66.8%independent
directors.
Next, Panel Breports information on the divestment statistics at the firm year level. With an
average of 1.263, the average divesting firm in our sample divests assets from more than one segment in a
single year. Only 15.1% of our divesting sample firms divests one or two full segments. When
aggregating the deal value of all the divested business units within a firm year, we find that the average
divesting firm receives $401 million for its divested assets, accounting for 11.7% of these firms’ total
assets.
Panel C shows CEO specific statistics. We find that 56% of the CEOs of the 608 divesting firms
have had at least one direct-experience segment within a firm year, which could reach to a maximum of
five direct-experience segments. We note that, since external hires cannot have a direct-experience
segment by construction, this percentage rises to 83% for the sub sample of internally-hired CEOs.In
terms of the industry work experience, 26% the CEOs have inside-industry experience segments and 28%
have outside-industry experience segments. The average CEO is 57 years old, and has been employed by
the firm for 18.5 years and as CEO for 7.6 years. Of our sample of CEOs, 68% are hired from inside the
11
firm and 10% are founders CEOs. CEOs hold on average around two titles (holding the president and/or
chairmantitle next to their CEO position).
Table 2 provides statistics for familiar and non-familiar segments in Panel A and for segments
from which firms divestversus fully retained segments in Panel B.
– Please insert Table 2 here –
Panel A shows that our total sample of 2,001 segments consists of 1,178 familiar segments and
823 non-familiar segments. CEOs have an average of 12.8 years of direct work experience in
574segments, 8.9 years of inside-industry experience in 234 segments, and 15.4 years of outside-industry
experience in 370 segments. On average, CEOs left their direct-experience segment5.8 years ago. We
classify all segments of founder CEOs as familiar segments in the form or direct experience, having an
increasing effect on the number of years of their direct work experience in a segment and a reducing
effect on the recency of their direct work experience. Our results indicate that familiar segments are
significantly larger and are more often core segments compared to non-familiar segments. One could
argue that boards are more likely to promote managers from core segments (which are typically the
largest segments operating in the same industry as the firm’s core industry) to a CEO position. The
statistics confirm that, indeed, a relatively large proportion of direct-experience segments are core
segments, but we still have 63.1% of non-core direct-experience segments. In Section 2.5, we discuss this
issue in further detail. We further find familiar segments to perform significantly better than non-familiar
segments in terms of industry-adjusted cash flows and to operate in industries with higher q. Familiar
segments receive more capital expenditures relative to the firm’s budget. Finally, the asset liquidity of
familiar segments does not differ from that of non-familiar segments.
Panel A further shows that the percentage of divesting segments does not significantly differ for
familiar compared to non-familiar segments.A more detailed analysis of the sub sample of divesting
segment years indicates that, on average, CEOs divest 1.33 business units from familiar segments and
1.36 from non-familiar segments. In addition, the absolute value of the aggregate transaction value of
these divestitures seem somewhat higher for familiar segments compared to non-familiar segments, but in
relative terms, a greater proportion of a segment’s assets gets divested from non-familiar segments. This
difference is not statistically significant though.
Panel B shows that in 608 firm years, firms divest assets from 768 segments and fully retain
1,233segments. The proportion of familiar segments does not significantly differfor the retained segments
(58.8%) compared to the divested segments (59%). This result also applies to the different levels of
familiarity.In contrast to the results of studies that focus exclusively on fully divested segments (e.g.,
Schlingemann, Stulz, and Walkling(2002); Dittmar and Shivdasani(2003)), we show that partially
divested segments are larger in terms of absolute size and relative size, indicating that they are too
12
important to be ignored. Larger segments often consist of a collection of smaller business units, thus
increasing the likelihood that a separable portionof a segment gets divested.
We also find the proportion of core segments, which are the largest segments in a firm year
operating in the same industry as the firm’s core industry, to be higher for the divested segments sample
compared to the retained segments sample. Although this result runs counter the motive for divesting
assets to increase the focus of the firm’s business (John and Ofek (1995)), it is consistent with a greater
likelihood to divest assets from larger segments.7Furthermore, divested segments have lower industryadjusted cash flows compared to retained segments, providing evidence in favor of two motives for
divesting assets: firms divest to reallocate assets to higher-valued users (Jain (1985)) and to obtain funds
when external financing is too expensive and internal financing is insufficient (Lang, Poulsen, and Stulz
(1995)).
2.5. Regression results
To examine whether CEO familiarity influences their decisions from which segments to divest assets, we
estimate binary logit regressions in which the dependent variable takes the value of one for divested
segments and zero for fully retained segments and Tobit regressions in which the dependent variable
equals the transaction value of all divested assets within a segment year to segment assets (winsorizedat
the 1-percentile and 99-percentile). We follow Schlingemann, Stulz, and Walkling (2002) for the
specification of the economic factors. We includesegment performance, investment, cross-subsidization,
and size, whether it is a core segment,8 segment q, whether the segment is less than 10% of total sales,
and segment industry’s liquidity. We also include year dummies and dummies for the number of
segments in which the firm operates as reported by the firm. The robust standard errors are clustered by
firm. Table 3 presents the results.
– Please insert Table 3 here –
The results of Regression (1) in Panel A largely corroborate the results of Schlingemann, Stulz,
and Walkling (2002). The table shows that CEOs are more likely to divest assets from segments with
7
We note that the focus argument applies to fully divested segments as the proportion of core segments in this sub
sample of fully divested segments drops to 6.1%.
8
In our tests we control for core versus non-core segments, based on the size of the segment and two-digit SIC
classification. In unreported robustness analyses we investigate several alternative proxies to distinguish segments
which may have a special status within the company from other segments: core segment based on two-digit SIC
only; core segments based on three-digit SIC only; and the largest segment based on sales. These alternative proxies
do not affect our results for the familiarity effect.
13
lower cash flows. The negative coefficient for cash flows supports both the efficiency and financing
rationales to divest (Jain (1985) and Lang, Poulsen, and Stulz (1995), respectively). Our coefficient of
industry median cash flows is significantly positive, which also suggests that firms divest assets when
their industry peers can manage these assets more efficiently.The probability of a divestiture is higher for
larger segments and for segments with higher imputed Tobin’s q.Our results further indicate the CEO’s
choice of divestment is not influenced by a segment’s capital expenditures, cross-subsidization, whether a
segment is the firm’s core segment, or whether a segment has sales of less than 10% of the firm’s
consolidated sales.
In the subsequent regressions, we examine the familiarity effect. We are interested in whether
CEOs’ level of familiarity with segments affects their selection of segments to divest assets from. A
preference for one segment over another – based on familiarity – can only appear when a CEO has
variation in his or her options, and thus variation in the levels of familiarity within a firm year. Therefore,
we generate an indicator variable that equals one for firm years with variation in familiarity, and zero
otherwise.9We add an interaction term between this variation variable and our familiarity proxies and
should find its coefficient to be significantly different from zero in the case of a familiarity effect.As
familiarity proxy, Regression (2) uses the aggregate familiarity dummy, which is a dummy for CEOs’
direct or industry work experience in a segment. We find a positive, but statistically insignificant
coefficient of the aggregated familiarity dummy of no-variation firms. More importantly, the interaction
term between the variation and familiarity dummy is significantly negative, indicating that CEOs are less
likely to divest assets from familiar segments than from non-familiar segments. Applying the Delta
method (as described inAi and Norton (2003)) on the interaction term suggests that the average
interaction effect is also statistically significant at a one-percent level.We also test whether the types of
experience makes CEOs more likely to divest from non-familiar segments. Regression (3) includes the
three dummy variables for direct-experience segment, inside-industry experience, and outside-industry
experience instead of a single dummy for familiarity and interacts those three dummy variables with our
9
Of our sample of 608 firm years, 270 firm years have no variation in familiarity. They miss variation for the
following reasons: 61 firm years have founders (including CEOs who started as executive officers after a spin-off);
14 firm years have internal hires and all segments are direct-experience segments; 26 firm years have internal hires
and all segments are outside-industry experience segments; 77 firm years have internal hires without any familiar
segments; 41 firm years have external hires and all segments are outside-industry experience segments; and 51 firm
years have external hires without any familiar segments.
14
variation-in-familiarity dummy.10Since the strongest form of familiarity is the CEO’s direct experience,
followed by inside-industry experience and outside-industry experience, we expect direct experience to
induce the strongest effect.The results indicate that the interaction term between the variation and directexperience dummy is the only statistically significant dummy with the predicted negative impact on the
segment selection choice.The Delta method confirms the significance of this negative impact with a pvalue of the average interaction effect equal to 0.015.
Because the interpretation of interaction coefficients in binary logit regressions is not trivial (see
Ai and Norton (2003)) and we need to segregate familiar segments from non-familiar segments within
firm years, the subsequent regressions exclude the 270 firms years without variation. As in the regressions
with the full sample, the results in Regressions (4) and (5) confirm that CEOs are significantly less likely
to divest assets from familiar segments compared to non-familiar segments and that this effect is mainly
present for the direct-experience segments. The familiarity effect is an economically significant finding.
The odds ratio of 0.645for the direct-experience coefficient indicates that divestments occur only 64.5%
as often among direct-experience segments, when compared to non-familiar segments. The coefficients of
the industry-experience variables are also negative, but not significant.11,12
It is interesting to note that for the sub sample of firms with variation in familiarity, CEOs are
more likely to divest assets from segments that receive less capital expenditures, but operate in industries
with higher capital expenditures, possibly to economize on cash flows. Divestitures are also more likely
to occur in segments with greater cross subsidization.
Panel B of Table 3 reports several robustness checks. The first set of robustness checks relates to
the literature that focusses on the impact of CEOs’ background on corporate decisions, where a distinction
is made between generalist and specialist CEOs (e.g.,Custódio, Ferreira, and Matos (forthcoming)). The
10
Regression (3) in Table 3 does not provide a coefficient for inside-industry experience for firm years without
variation in familiarity, because this type of experience only appears in firm years with variation in familiarity.
Specifically, inside-industry experience segments are always related to a direct-experience segment, creating
variation in familiarity within a firm year. If a CEO does not have a direct-experience segment, he does not have an
inside-industry experience segment by construction.
11
In unreported analyses, we find that our direct-experience effect also holds for a regression in which we only
include the direct-experience dummy, instead of the three mutually exclusive familiarity dummies. In this test, the
direct-experience coefficient equals -0.334 with a p-value of 0.041.
12
We also investigate a related question, which is whether the presence of familiar segments affects the proportion of
assets that firms divest. In regression models explaining total transaction value over assets at a firm-year level, we
find that none of our firm-level familiarity measures has a significant effect. Results are available upon request from
the authors.
15
familiarity effect in our study can occur both in firms ran by generalist CEOs (as part of their broad
background) and specialist CEOs (when the CEO is specialist in the to-be-divested segment). However,
the effect can also be absent in both groups, when generalists have general experience, but not per se
specifically related to the segment or when specialists’ skills and experience do not relate to the divesting
segment. Therefore, the overlap between our definitions and their specialist-generalist distinction is not
predetermined and remains an – interesting – empirical question.
To test whether our documented familiarity effect is more (or less) apparent in generalist or
specialist firms, Regressions (1) and (2) interact our direct-experience dummy with a specialist and,
respectively, a generalist dummy. We use two different definitions to distinguish between specialists and
generalists. First, we define specialist CEOs as CEOs that have direct work experience in one (and not
more than one) segment within a firm year. 53% of all segments are managed by specialist CEOs under
this definition. Second, we define generalist CEOs as CEOs that are familiar with all segments within a
firm year, but, since we need variation in familiarity within a firm year, in different levels of familiarity
(26% of all segments are run by generalist CEOs under this condition).The regression results show that
the interaction terms in both regressions are not significantly different from zero, suggesting that the
familiarity effect does not show up differently in specialist or generalist firms.
In the second set of robustness checks, we account for the size of the divested business
unitsrelative to the size of the segments. Regressions (3) until (5) estimate Tobit regressions with as
dependent variable the sum of the transaction value of all divested business units in a segment year to
beginning of the year segment assets, winsorized at a the 1- and 99-percentile. The relative transaction
size equals zero for segment years without divestiture. Because SDC does not report the transaction value
of all the divestitures, we retain the divesting segment years with at least one available transaction value
in the analysis, but we eliminate divesting segment years in which SDC reports transaction values for
none of the divested business units. We account for divesting segment years with some, but not all
missing transaction values, by adding to Regression (5) an indicator variable that equals one in such
instances and zero otherwise.13In line with a smaller likelihood to divest from familiar segments, the
results in Regressions (3) to (5) suggest that CEOs also tend to divest a smaller proportion of assets from
13
It is interesting to note that the coefficient of this indicator variable is positive, suggesting that firms are less likely
to disclose the transaction value of a divestiture when they divest another relatively large business unit from the
same segment in that same year.
16
familiar segments relative to non-familiar segments. Again, this result is mainly driven by a CEO’s direct
work experience in the segment.14
In a final set of robustness tests (unreported), we provide a more detailed analysis of the role of
core segments in the familiarity effect. If boards mainly promote managers with work experience in core
segments to a CEO position, the negative coefficient of the direct experience dummy in our reported
regressions might reflect firms’ decision to focus on their core business, suggesting that our documented
familiarity effect is a mechanical relation. We deal with this issue in two ways. First, we add an
interaction term between the direct-experience segment dummy and core-segment dummy to our basic
regression as reported in Model 5 of Table3 (Panel A). We find that the coefficient of the directexperience segment dummy remains significantly negative. Moreover, the interaction term is significantly
positive with a p-value equal to 0.013(the average interaction effect is also significant at the five-percent
level when applying the Delta method), contrary to the prediction of a mechanical relation. According to
the mechanical relation explanation, we would expect the familiarity effect to prevail in core segments.
However, these regression results suggest that the familiarity effect is mainly present in non-core
segments, while it is not present in the core segments.
As a second test for the mechanical relation explanation, we construct two separate samples of
firm years: (1) firm years in which CEOs got promoted from the core segments (i.e., firm years in which
the direct-experience segments are also core segments); and (2) firm years in which CEOs got promoted
from non-core segments (i.e., firm years in which the direct-experience segments are non-core
segments).For the mechanical relation explanation to hold, the familiarity effect should prevail in the first
sample and not the second. Although we find a negative direct-experience coefficient in both sub
samples, the coefficient is only significant in the sub sample of firm years with CEOs that got promoted
from non-core segments (with a p-value below one percent). Overall, these extra tests do not support a
significant mechanical relation between familiarity and a segment’s likelihood to divest.
3. What explains the familiarity effect?
14
In unreported robustness tests, we estimate the same binary logit regressions as Regressions (4) and (5) of Table 3,
Panel A, but for different sub samples of firm years dependent on different transaction sizes: i.e., firm years with
divestitures of complete segments (as in Schlingemann, Stulz, and Walkling (2002); firm years in which the divested
assets in a segment reach a transaction value of at least $10 million; firm years in which the divested assets in a
segment reach a transaction value of at least $50 million. The familiarity effect holds in all three sub samples.
17
So far, our analysis demonstrates an economically and statistically significant effect: CEOs are less likely
to divest assets from their direct-experience segments. In this section we investigate three possible
explanations for this effect.
3.1. The board’s CEO selection
Considerations in the CEO selection process may be the underlying explanation for the familiarity effect.
In the selection process for a new CEO, the board of directors takes into account a candidate’s prior work
experience. For instance, the board may choose a CEO to grow and focus on the familiar business
segment as a part of the firm’s long-term strategy, where the non-familiar segments are strategically less
important. As a result, CEOs are less likely to divest assets from their familiar segments. Huang (2010)
investigates divestitures of complete segments in relation to an industry-based measure for the match
between CEO expertise and retained assets and finds evidence suggesting that the appointment of CEOs
improve firm performance in the case of a match between the CEO’s expertise and the firm’s assets.
We testin two ways whetherCEO selection explains our familiarity effect. The first test is based
on a CEO’s tenure.CEOs are least likely to divest assets representing their skill sets shortly after their
appointment (Weisbach (1995)).Moreover, within two or three years of tenure, CEOs have acquired
considerable knowledge from different sources and gained more political leeway to deviate from their
original mandate (Hambrick and Fukutomi (1991)). Therefore, if the board’s CEO selection explains the
familiarity effect, we expect this effect to occur early in the CEO’s tenure. We test this hypothesis by
splitting the sample into newly-hired CEOs with tenure of up to two years and longer-tenured CEOs with
tenure of three or more years. We choose the two-year cutoff point to remain consistent with the
arguments of Hambrick andFukutomi (1991). Changing the threshold to three years does not change our
conclusions. Regression (1) of Table 4 shows the results for newly-hired CEOs and Regression (2) for
longer-tenured CEOs.
–
Please insert Table 4 here –
Contrary to the CEO selection hypothesis, we find that newly-hired CEOs do not show a familiarity
effect. Rather, our results suggest that longer-tenured CEOs exhibit a familiarity effect. The odds ratio of
0.512 suggests that direct-experience segments of longer-tenured CEOs experience 48.8% fewer
divestitures than do non-familiar segments.
For the second test of the selection explanation, we take two steps to distinguish between the
board’s selection decision and the CEO’s divestment decision. In the first step we estimate a model to
predict which segment is likely to be divested. We use our standard model as presented in Regression (1)
of Table 3 (Panel A) to estimate the likelihood of a segment to be divested. We take the full sample of
firm years rather than the sub sample of firm years with variation in familiarity to minimize the likelihood
18
that any form of familiarity would affect the determinants of segment divestment. For a stronger
specification to test our selection explanation, Regression (2) only incorporates segments of firm years
with CEOs who succeed to that position one or two years prior to the divestiture. Regression (3) goes one
step further by using the sub set of divesting firm years with externally hired CEOs with tenure up to two
years.15Theskills of the latter group of CEOs should relate to the remaining assets and less correlated with
recently divested assets.
In the second step, we use the first-step coefficients to predict the probability of a segment to
divest assets using information from one year prior to the CEO appointment. If CEOs were hired to grow
their direct-experience segment, the predicted probability of a segment to divest should be the lowest
(highest) for the direct-experience segments (non-direct-experience segments). Because the year of a
CEO's appointment can occur prior to 1996 and our estimation period is from 1996 until 2004, we do not
control for the year in which the divestiture takes place in the first-step regression.Our sample contains 88
CEO-firm combinations and 196 segment years.16 Table 5showsthe results.
–
Please insert Table 5 here –
Panel A provides the coefficients for our three regression models that estimate the predicted probability of
a segment to divest. Panel B shows the statistics of the predicted probability per level of familiarity in the
year prior to the CEO’s appointment, based on all three regression models in Panel A. With an average
predicted probability of 41.5% and a median of 40.0% (using Regression (1) from Panel A), our results
suggest that, contrary to the hypothesis, the board appoints CEOs whosedirect-experiencesegments that
have the highest predicted probability to be divested. Moreover, thedirect-experiencepredicted
probabilities are significantly higher than that of non-familiar segments (p-value equals 0.001), which is
also contrary to the selection explanation.Our results are robust to using different regression specifications
(as documented in Panel A);as with none of the specifications the average or median segments’ predicted
15
Our results also hold when estimating a regression based on a sub set of divesting firm years with all externally
hired CEOs (so, both newly-hired and longer-tenured external CEOs) (results are unreported).
16
The sample of firm years with variation in familiarity contains 177 CEO-firm combinations. We remove 89
combinations for the following reasons: for 41 CEO-firms no financial data is available or we observe a change in
segment reporting (we lose firm years even when we miss information of one segment within that whole firm year);
for 19 CEO-firms, the firms report one segment when the CEO was appointed; for three CEO-firms, we cannot
download that information from Compustat, because the CEO was appointed prior to 1979; for five CEO-firms no
Compustat coverage exists in the year prior to appointment; for five CEO-firms we lose variation in familiarity
between segments; and for 16 cases the CEO has been appointed in the year of the divestment (when including these
observations theestimation of the model and predicted probability would be based on the same data).
19
probability to divest is significantly larger for direct-experience segments compared to non-familiar
segments.We further find a significantly higher average predicted probability to divest from outsideindustry experience segments,and a similar predicted probability to divest from inside-industry experience
segments, compared to non-familiar segments.
Overall, our evidence is not in line with the CEO selection explanation for the familiarity effect.
Instead, our results suggest that the board selects CEOs to restructure their direct-experience segments.
But eventhough CEOs are predicted to divest assets from their direct-experience segments when
appointed as CEO, we show that, shortly after their appointment, they do not act as predicted. Moreover,
later in their tenure CEOs act in the opposite way than predicted by divesting assets from non-familiar
segments, while retaining the familiar segments.
3.2. Managerial entrenchment
CEOs might favor familiar segments based on agency theory. One way for CEOs to maximize their utility
is by facilitating their entrenchment. CEOs can entrench themselves by investing in assets that are
complementary to their skills, thus becoming more valuable to the shareholders and making it more costly
to replace them (Shleifer and Vishny (1989)). CEOs can achieve the same end by divesting assets from
non-familiar segments and increasing the share of familiar assets.
Because newly-hired CEOs are the agents who experience the highest marginal benefits from
actions to entrench themselves, our finding that newly-hired CEOs do not show a significant familiarity
effect is preliminary evidence against the entrenchment explanation. We perform twoadditional tests for
managerial entrenchment, where we consider entrenchment related to external and internal governance.
First, as external governance measure, we use the entrenchment index of Bebchuk, Cohen and Ferrell
(2009), which contains six provisions that isolate executives from being acquired and from shareholders’
ability to impose their will on them. If entrenchment explains the familiarity effect, we expect a stronger
familiarity effect for more entrenched CEOs. We note that, although one could argue that already
entrenched CEOs do not need to divest from non-familiar segments to facilitate their entrenchment, we
expect entrenched managers, having to choose which assets to divest, not to take actions that could undo
their entrenchment. To test this hypothesis, we include an interaction term with the directexperiencedummy and a dummy for an above sample median entrenchment index (i.e., equal to three or
higher).
Regression (1) of Table 6 shows the results.
– Please insert Table 6 here –
20
CEOs of firms with a high entrenchment index do not show a stronger familiarity bias, which is not in
line with the entrenchment explanation. The Delta method confirms this non-significant interaction
effect.Our direct-experience dummy remains significant and negative.17
For our second test of the entrenchment explanation we use a measure for internal corporate
governance. We expect good corporate governance to induce CEOs to make value-maximizing decisions
for their firm, rather than for themselves. CEOs of firms with more independent directors have less power
over the board; hence, they have less discretion over their decisions (e.g., Ryan and Wiggins III (2004),
Moeller (2005), and Paul (2007)). If CEOs exhibit a familiarity effect due to their desire to entrench, they
would not be able to exhibit that bias in well-governed firms. Regression (2) adds an interaction term
consisting of the direct-experience segment dummy and a dummy that takes the value of one for firm
years with an above-median percentage of independent directors in the board(a dummy for the highest
quartile of independent directors provides similar results). The interaction term does not show up
significant (neither does the average interaction effect, as estimated with the Delta method of Ai and
Norton (2003)) and our direct-experience dummy remains significant.18 These results are not in line with
managerial entrenchment as explanation forthe direct-experience effect for longer-tenured CEOs.
3.3. CEOs’ information environment
Another explanation for CEOs’ reluctance to divest from their direct-experience segment is that their
information sets among the segments vary depending on their familiarity with the segments. For this
explanation, we view asset divestitures from segments as a negative capital allocation decision within an
internal capital market in which segment managers negotiate with CEOs about their share of the budget.
From the point of view of segment managers, their empire-building tendencies (as in Jensen (1986) and
Stulz (1990)) give them incentives to bargain for a greater share of the budget and to overstate their
17
Using the CEO’s number of titles as a proxy for entrenchment (as in Morck, Shleifer and Vishny (1989), Adams,
Almeida, and Ferreira (2005), and Fracassi and Tate (2012)) provides similar results.
18
We note that although an independent board is a better monitor than a more dependent board, its effectiveness
depends on the information that the CEO provides (Song and Thakor(2006) and Adams and Ferreira (2007)). A
CEO may be especially reluctant to share relevant information on divestment decisions that are motivated by the
CEO’s desire to entrench, which could explain the non-significant result. As additional robustness tests, we use an
alternative governance measure, which is based on CEO ownership.CEOs with a higher stake in their firm have
more decision-making power (Finkelstein(1992) and Adams, Almeida, and Ferreira (2005)). On the other hand, a
CEO’s incentives are more aligned with shareholders’ incentives when they own a higher percentage of the firm’s
shares (Jensen and Meckling(1976)). We find that both above- and below-median CEO ownership does not
influencethe home-base effect(results are available on request).
21
investment opportunities (e.g., Milgrom and Roberts (1988), Jensen (2003) and Wulf (2009)). They are
more likely to succeed when they are better informed than the CEOs. However, CEOs account for this
information asymmetry by scaling down the capital allocation to segments where segment managers have
a greater information advantage (e.g., Harris and Raviv (1996, 1998); Bernando, Cai, and Luo (2001);
Duchin andSosyura (2010)). In our case, there is less asymmetric information between managers of
familiar segments and their CEOs and thus the CEOs are less inclined to divest familiar segments where
they are more confident of the valuation.
The impact of the different levels of CEOs’ familiarity with segments on the likelihood to divest
from these segments is in line with the information asymmetry explanation for our familiarity effect,
because CEOs are most informed about their direct-experience segments. CEOs’ previous employment in
thedirect-experience segment provides them with detailed knowledge of its industry (as with the industryexperience segments), but also of its operations, procedures and employees. In addition, in the period they
worked in their direct-experience segment, they came to know the value and growth opportunities of its
assets. Regression (5) in Table 3 (Panel A) shows that, although all three familiarity coefficients are
negative, the only significant coefficient is that of the direct-experience segment. This finding is in line
with the conjecture that CEOs are more likely to divest from segments with greater information
asymmetry between segment managers and CEOs.
We perform three additional tests to further investigate the information explanation. The first test
is based on the assumption that CEOs’ knowledge of a business segment cumulates with time, i.e.
increases with the number of years in their direct-experience segment. If information asymmetry between
CEOs and segment managers explains the familiarity effect, the relation between the number of years of
work experience in their direct-experience segment should reduce asymmetric information; hence
negatively affect the probability of divestment. We replace the direct-experience dummy with three
variables for terciles of the number of years the CEO has experience in the direct-experience segment,
i.e., up to three years, from four to sevenyears and eight years and longer.
Table 7 presents the results twofold. In Panel Awe regress the segment-divestment dummy on the
two highest terciles of the direct experience dummy and only include the sub sample of direct-experience
segments. This way, we can test whether the likelihood to divest from direct-experience segments is
significantly stronger for segments in which CEOs have worked a longer period of time. In Panel B, we
report the logit regressions of the full sampleas in Regression (5) of Table 3, but replace the directexperience dummy for the three terciles of the length of CEOs’ direct experience.
– Please insert Table 7 here –
The results in the first regression of Panel A imply that the familiarity effect does not depend on
the years of direct experience in a segment. In Panel B, Regression (1) shows negative coefficients of all
22
three direct-experience terciles (though the first and third tercile have a p-value of 0.152 and 0.154,
respectively). The effect of direct work experience seems to be strongest for CEOs with four to seven
years of experience in the segment, but a Wald test rejects the hypothesis that the coefficient for the
second tercile significantly differs from that of the first or third tercile (with p-values greater than 0.664).
This resultsuggests that CEOs’ accumulated knowledge on direct-experience segments does not affect the
probability to divest from these segments.
In our second additional test of the information explanation, we use the number of years since the
CEO left the direct-experience segment. Over the years, CEOs’ superior information on their directexperience segments is likely to fade, while they gain more knowledge of the non-familiar segments. This
change has a diminishing effect on the difference in the degree of information asymmetry between CEOs
and non-familiar segment managers and between CEOs and familiar segment managers. This test also
measures the relevance of a particular version of CEO familiarity, where a CEO may be disinclined to sell
from segments with personnel that previously worked under them out of a sense of loyalty and friendship.
As we expect that these relations should fade over time due to retirements and other personnel turnover,
we predict that the familiarity effect should decline with the time since they left the segment. In
Regressions (2) of Table 7 (Panel A and B) we consider the number of years since the longer-tenured
CEO left the segment, again based on terciles: up to four years ago, five to nine years ago, and over ten
years ago. Our results suggest that the familiarity effect does not fade over time. In fact, the coefficients
tend to become more negative the longer ago the CEO left the segment, thougha Wald test rejects a
significant difference between the three coefficients (p-values greater than 0.309).Perhaps, the familiarity
effect is persistent, because CEOs’ store of knowledge on their direct-experience segments makes it
relatively less costly for them to keep up with the direct-experiencesegments and their respective
industries,in comparison to what they have to do to acquire knowledge on other non-familiar segments.
In our third additional test for the information explanation, we account for the number of years a
CEO has corporate work experience within the firm prior to his appointment as CEO. With so-called
corporate work experience, we refer to the number of years a CEO has work experience in the firm’s
headquarters. The underlying rationale is that the longer managers have corporate work experience, the
more time they have to learn about their non-familiar segments. As information asymmetry between
CEOs and non-familiar segment managers reduces over time, we expect the familiarity effect to diminish
the longer CEOs have corporate experience prior to their appointment. In Regressions(3) of Table 7, we
split the direct-experience segments into three groups: direct-experience segments where CEOs do not
have corporate work experience prior to their appointment as CEO (so they got promotion to CEO
directly from their direct-experience segment) or have one year of corporate work experience; directexperience segments where CEOs have corporate experience between two and four years; and direct-
23
experience segments where CEOs have five or more years of corporate experience. Although we find all
three coefficients to be negative, the familiarity effect is significantly stronger for CEOs with two to four
years of corporate experience compared to less than two years (p-value of the Wald test equals 0.040), or
more than four years of corporate experience (p-value equals 0.042). In addition, the coefficient of third
tercile with most years of corporate experience lost significance, which is weak evidence consistent with a
diminishing familiarity effect after several years of corporate experience.
Overall, our results suggest that the variation in information sets among different segments
induces CEOs to divest from non-familiar segments. However, the lower level of CEOs’ effort necessary
to keep up with their direct-experience segments relative to other segments might explain our findings
that the familiarity effect is not related to the number of years of work experience in the segment, does not
fade over time, and is weakly related to the CEO’s corporate work experience.19
4. The value-relevance of familiarity
Our results so far suggest that neither selection nor entrenchment drives the familiarity effect, but the
variation in CEOs’ information sets among segments. We find that the familiarity effect is significant for
longer-tenured CEOs and pertains to their direct-experience segments. This section investigates how the
stock market perceives these familiarity effects by means of an event study.On the one hand, divesting
from non-familiar segments can generate higher abnormal returns, because managers have less
knowledge how to best manage and improve the performance of these assets and therefore there is greater
potential gain to higher-valued users, who may be willing to pay a higher price. On the other hand, we
expect returns of divestitures from direct-experience segments to be higher than the returns of divestitures
from non-familiar segments, as CEOs can use their superior information to pick winners (Stein (1997)) by
19
A possible alternative explanation for this persistent familiarity effect is that, instead of being more informed
about their familiar segments, CEOs may have a perception of being informed about their familiar segments, which
is based on an illusion of control. According to Langer (1975), familiarity enhances the illusion of control. This
illusion leads individuals to overestimate the likelihood of a successful outcome of their decisions (Langer (1975))
and to be too optimistic about the likelihood of both positive and negative events (Weinstein (1980)). With respect
to our familiarity effect, CEOs’ familiarity with segments can make them prone to the illusion of control, inducing
them to underestimate the familiar segments’ risks and overestimate their future returns. As a result, they are less
likely to divest from familiar segments. Over the years, CEOs’ superior information relative to the non-familiar
segments is likely to fade, while their illusion of control is likely to remain. The illusion of control may therefore be
an explanation for the home-base effect that CEOs exhibit even though they left their direct-experience segment a
long time ago or have at least four years of corporate work experience. Although this story is a plausible explanation
for our findings, we cannot design a specific empirical test to provide evidence for this illusion of control.
24
divesting underperforming assets from their direct-experience segment or exploit an overvaluation of
these assets. In addition, CEOs that divest from their direct-experience segments have a comparative
advantage in locating and bargaining with potential buyers.Previous studies provide empirical evidence in
line with these arguments by showing that CEOs generate higher abnormal returns when acquiring firms
that are in close proximity to the acquirer (Uysal, Kedia, and Panchapagesan (2008)) or that operate in
industries in which CEOs have work experience (Custódio and Metzger (2012)).
Our sample has in total 1,032divested business units. Of those announcements, we have
909observations with available return data on a unique divestiture date.20To avoid the possibility that
outliers drive our results, we winsorizeCAR at a one- and 99-percentile.In Table 8, Panel A provides the
abnormal return statistics for this sample of 909 observations, but also for a sample of 420 observations
for which we have complete information.We end up with 420 observations, because we lose 424
observations due to missing transaction values and another 65 observations due to missing values for
industry-adjusted capital expenditures and CEO equity ownership. Panel A further reports statistics for
the subsamples of divestitures from direct-experience, inside-industry-experience, outside-industryexperience, and non-familiarsegments. Since longer-tenured CEOs mainly exhibit the familiarity effect,
we also split the sample into longer-tenured and newly-hired CEOs.
– Please insert Table 8 here –
Our results show an average positivemarket response to divestiture announcements, whichvaries across
different types of divestitures. In the sample with fully available information, we find that on average the
direct-experience divestitures generate 0.55% abnormal returns, while the non-familiar divestitures
generate higher abnormal returns, amounting 0.79%. The difference is not significantly different though
(p-value equals 0.656). Splitting our sample into divestitures announced by newly-hired and longertenured CEOs provides a different view; firms with longer-tenured CEOs that announce a divestiture from
their direct-experiencesegment generate a positive average abnormal return of 0.70%, while this
percentage is 0.50% for divestitures from their non-familiar segments.Again, this difference is not
statistically significant at a less than ten-percent level. With 1.96%, non-familiar divestitures announced
by newly-hired CEOs generate the highest abnormal returns.
20
By unique divestiture date, we refer to the cases in which SDC reports two or three separate divestiture
announcements taking place at the same date in the same segment (but for separate business units). In such
instances, we aggregate the information to one observation. This aggregation reduces 62 divestiture observations to
27 divestiture announcements for the event study. Of the remaining 997 observations, we lose 88 observations due
to missing abnormal returns values.
25
Panel B of Table 8 shows estimates of ordinary least squares regressions where we regress the
three-day abnormal returns on the familiarity dummies and several control variables. We follow Bates
(2005) for the specification of the control variables, which are: the relative transaction size, the firm’s
Tobin’s q, industry-adjusted capital expenditures, industry-adjusted leverage and industry-adjusted cash,
and the percentage of stock owned by the CEO. We use the 420 observations with available information,
because it is crucial to know the relative transaction size(the variable responsible for the biggest drop in
observations) to account for the economic significance of the divestitures. The average (median)
transaction value of the divestitures amounts 6.0% (1.5%) of the book value of the firm’s total assets,
while the 25- and 75-percentiles are, respectively, 0.3% and 6.0%.
Regression (1) shows a positive direct-experience coefficient, suggesting that direct-experience
divestitures generate 0.3% higher abnormal returns than non-familiar divestitures. However, the
coefficient is not significant. The effects of inside- and outside-industry experience are also insignificant,
with very small coefficients.Since divestitures that increase a firm’s focus are positively related to firm
performance (Berger and Ofek (1995)), we add the variables excess value and a core dummy to
Regression (2). We do not find a significant core-segment or excess-value effect on abnormal returns. The
direct-experience effect remained unaffected. Regression (3) adds two indicator variables for divestitures
announced by founder CEOs and by internally-hired CEOs, because we assume that founder CEOs have
direct work experience in all the segments of their firm and thus all divestitures by founder CEOs are
direct-experience divestitures, and CEOs can have divestitures from direct-experience and inside-industry
experience segments only when they are hired internally, possibly affecting the insignificant directexperience coefficient. We find that the abnormal returns of divestiture announcements by founder CEOs
are 2.8% lower and that of internally-hired CEOs 1.5% lower. More importantly, after adding these two
dummy variables, the direct-experience coefficient increases to 1.0% and, with a p-value equal to 0.114,
almost significant at the ten-percent level.
Because we find that longer-tenured CEOs exhibit a familiarity effect, we split the directexperiencedummy into a direct-experience dummy of longer-tenured CEOs and one of newly-hired CEOs
in Regression (3). We find thatshareholders reward direct-experiencesales made by longer-tenured CEOs
with an economically and statistically significant 1.6%. Even though longer-tenured CEOs tend to be
reluctant to sell assets from their direct-experience segments, their superior information helps them to
generate higher abnormal returns if they act against their disinclination. The direct-experience dummy is
not significant for the newly-hired CEOs.21
21
We did an additional robustness analysis on the positive direct-experience effect on abnormal returns (results are
not reported). We add to Regression (3) an interaction term between the direct-experience and the core-segment
26
In separate (unreported) regression analyses, we examine the distribution of the transaction
surplus between divesting firms and buyers. If superior information gives familiar CEOs more bargaining
power, we should observe the divesting firms to get a bigger proportion of the takeover surplus. We
follow Kale, Kini, and Ryan (2003) and construct a measure for the fraction of the wealth gain received
by the divesting firm. We note that, for the calculation of the transaction surplus, we can only focus on
deals with US publicly listed buyers with available return information, restricting our sample to 303
divestitures.However, this sub sample shrinks to 181 observations after deleting deals for which we miss
values of the independent variables(we lose 102 observations due to missing values on the relative
transaction size).We first measure the dollar wealth gains for the divesting firm (WD) and the buyer
(WB), by multiplying their respective three-day abnormal returns and their market capitalizations four
weeks prior to the divestiture announcement. Next, we define the total wealth gain to be the sum of the
gains for buyer and divesting firm (WT=WD+WB). In the case the total wealth gain is positive, we define
the proportion of gain received by the divesting firm to be WD/WT, and otherwise 1-WD/WT. The
proportion of the gains is winsorized at the 1-percentile and 99-percentile and has an average (median)
value of 0.55 (0.50) for the 181 observations with available information. We then perform the same
regressions as in Table 8, but replace CAR for this measure of the divesting firm’s proportion of the
synergy gains. We find that, consistent with the information advantage of familiar CEOs, longer-tenured
CEOs gain bybargaining for a greater share of the takeover surplus when divesting assets from directexperience segments than from non-familiar segments (coefficient equals 0.622, p-value equals 0.056).
5. Conclusion
Our paper examines the impact of CEOs’career paths on corporate decisions. We focus on how CEOs’
familiarity with segments influences their divestment decisions.CEOs are familiar with a segment due to
dummy and find no significant interaction term. However, when running the same regression for the sub sample of
divestitures announced by longer-tenured CEOs, we find that the positive familiarity effect can be attributed to the
divestitures from non-core direct-experience segments (coefficient equals 0.019, p-value equals 0.076), which are
exactly the divestitures that CEOs are less likely to make. Second, we replace the internally-hired-CEO and founderCEO dummy in Regressions (3) and (4) for an interaction term between the direct-experience dummy and a dummy
for firm years with variation in familiarity. The interaction term in Regression (3) has a coefficient equal to 0.025,
which is significant at the five-percent level, suggesting that CEOs with variation in familiarity generate 2.5%
higher abnormal returns when announcing a direct-experience divestiture then when they announce a non-familiar
divestiture. The two interaction terms in Regression (4) indicate that this significant interaction effect can be
attributed to the divestiture announcements of longer-tenured CEOs.
27
their work experience in the segment or in its industry. We empirically show that CEOs are less likely to
divest assets from familiar segments relative to non-familiar segments.The familiarity effect mainly
manifests itself among segments in which CEOs have direct work experience.
After documenting the familiarity effect, we explore three potential non-mutually exclusive
explanations: the board’s CEO selection, CEO entrenchment, and the CEO’s information environment.
We find no evidence consistent with the CEO selection and CEO entrenchment explanations. Rather, our
evidence is in line with the conjecture that CEOs are more likely to divest assets from non-familiar
segments than familiar segments, because the information asymmetry between CEOs and non-familiar
segment managers is greater.
Our event study suggests that, on average, firms are capable of creating value for shareholders by
divesting assets. Moreover, CEOs’ superior information about their direct-experience segments provides
them a comparative advantage in finding higher-valued users for assets that belong to their directexperience segment and in negotiating about the deal, because direct-experience divestitures generate the
highest abnormal returns. However, the familiarity effect can be costly to shareholders, as these
divestitures are the least likely to occur.
28
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Table 1: Firm and CEO summary statistics
The table shows the means, standard deviations, minimum, and maximum values, and the number of
observations of firm, divestiture, andCEO variables. Leverage is debt divided by total assets. Net capital
expenditures are the gross capital expenditures minus depreciation and amortization. We define cash
flows as operating profit plus depreciation and amortization. Tobin’s q is the ratio of the market-to-book
value of assets, as calculated in Malmendier and Tate (2005, 2008). We calculate the excess value
measure as in Berger and Ofek (1995); the diversity in q as in Rajan, Servaes, and Zingales (2000); and
the entrenchmentindex as in Bebchuk, Cohen, and Ferrell (2009). Independent directors is the percentage
of independent directors relative to all directors. Fully divested segments are segments that are completely
divested and firms cease to report these segment post divestiture. The aggregate transaction value is the
sum of the transaction values of all divested business units within a firm year. Direct-experience segments
are segments in which the CEO worked prior to his appointment as CEO. Inside-industry experience
segments are segments that operate in the same two-digit SIC industry as the direct-experience segments.
Outside-industry experience segments are segments that operate in the same two-digit SIC industry as the
firms for which the CEO used to work prior to joining his current firm.
Mean
SD. Minimum Maximum
N
Panel A. Firm summary statistics
Sales t-1 ($M)
Assets t-1 ($M)
Leverage
Capxt-1/sales t-2
Cash flow t-1/sales t-2
Tobin's q
Number of business segments
Diversity in q
Excess value
Entrenchmentindex
Independent directors
7,249
9,401
0.638
0.014
0.174
1.665
3.291
0.104
0.004
2.436
0.668
13,869
23,952
0.155
0.111
0.120
0.864
1.105
0.109
0.539
1.364
0.177
41
109
0.190
-0.593
-0.087
0.241
2.000
0.000
-1.386
0
0.083
153,627
279,097
1.000
2.008
1.716
7.302
7.000
0.492
1.386
5
0.923
608
608
608
608
608
608
608
595
555
608
528
Panel B. Divestment statistics
Number of segments with a divestment
Dummy for firm years with a fully divested segment
Number of segments with a fully divested segment
Aggregate transaction value of all divested business units ($M)
1.263
0.151
0.163
401
0.523
0.359
0.400
967
1
0
0
0
4
1
2
10,837
608
608
608
401
Aggr. transaction value of all divested business units/assets t-1
0.117
0.266
0
Panel C. CEO summary statistics
Dummy for presence of a direct-experience segments
Number of direct-experience segments
Dummy for presence of inside-industry experience segments
Number of inside-industry experience segments
Dummy for presence of outside-industry experience segments
Number of outside-industry experience segments
Years employed as CEO
Years worked for firm
Number of titles (CEO, president, chairman)
Internally hiredCEO
0.559
0.939
0.262
0.385
0.280
0.609
7.610
18.495
2.219
0.676
0.497
1.094
0.440
0.753
0.449
1.159
9.090
13.010
0.520
0.468
0
0
0
0
0
0
0
0
1
0
3.508 401
1
5
1
5
1
6
54
54
3
1
608
608
608
608
608
608
608
594
526
608
33
Founders
CEO age
0.100
57.072
0.301
7.376
0
36
1 608
90 608
34
Table 2: Characteristics of sub samples
The table presents means, standard deviations, number of observations, and mean differences for segments of the fiscal year prior to the divestiture
announcement. Panel Acompares familiar and non-familiar segments and Panel B compares segments from which CEOs decide to divest and fully
retained segments.Proxies for familiarity are the CEOs’ direct experience, inside-industry experience, and outside-industry experience segments.
The size<10% dummy indicates that segment sales are less than 10% of firm sales. The core segment dummy equals one for the largest segments
within a firm year based on sales with the same primary two-digit SIC code as the primary two-digit SIC code of the firm. Cash flows are the
segment’s operating profit plus depreciation and amortization. We define net capital expenditures as the gross capital expenditures minus
depreciation and amortization. We define the cross-subsidization variable as in Ahn and Denis (2004). The segment’s Tobin’s q represents the
median industry q of all Compustat firms with the same two-digit SIC code as the segment (q is the ratio of the market-to-book value of assets, as
calculated in Malmendier and Tate (2005, 2008)). Segment’s industry liquidity is the liquidity index at the two-digit SIC code level, as calculated
by Schlingemann, Stulz, and Walkling (2002). We define the industry-adjusted variables as the segment variable minus the median of all
Compustat firms with the same two-digit SIC code. Fully divested segments are segments that are completely divested and firms cease to report
these segment post divestiture. The aggregate transaction value is the sum of the transaction values of all divested business units within a firm year.
The aggregate transaction value to segment sales and the aggregate transaction value to segment assets are winsorized at the one- and 99percentile. The subscripts refer to the year relative to the year in which firms announce their divestment. We truncate ratios at -1 and +1. ***, **,
and * denote that the means of familiar (divested) segments are significantly different from the means of non-familiar (retained) segments at the 1
percent, 5 percent, and 10 percent level, respectively.
Panel A: Familiar versus unfamiliar segments
Familiar
Direct experience
Inside industry exp. Outside industry exp.
Mean
SD.
N Mean
SD.
N Mean SD.
N
Aggregate familiarity dummy
1.000 0.000 574 1.000 0.000 234 1.000 0.000 370
Years direct experience
12.843 13.440 574
234
370
Years since left direct exp. segment 5.828 7.441 558
.
.
Years inside-industry experience
574 8.910 8.891 234
370
All
Mean
SD.
N
1.000 0.000 1178
6.258 11.366 1178
1.770 5.319 1178
Years outside-industry experience
Sales t-1($-mln)
Sales t-1/ firm sales t-1
Size<10% dummy
Core segment
Cash flow t-1/sales t-2
3,335
0.371
0.136
0.369
0.205
9,764
0.247
0.343
0.483
0.177
574
572
574
574
574
565
2,244
0.218
0.261
0.103
0.171
4,225
0.153
0.440
0.304
0.181
234 15.351 9.281
370
4.822
8.822 1178
234
234
234
234
231
370
370
370
370
366
2,603
0.323
0.167
0.284
0.184
7,212
0.229
0.373
0.451
0.189
1,699
0.315
0.157
0.268
0.160
2,263
0.216
0.364
0.443
0.208
1176
1178
1178
1178
1162
Not familiar
All
Mean
.
1,693 ***
0.277 ***
0.204 **
0.202 ***
0.187
SD.
-
N
823
823
823
-
823
3,647
0.218
0.403
0.402
0.208
822
822
822
823
815
35
Industry-adj. cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry-adj. capxt-1/sales t-2
Cross-subsidization
Segment's Tobin's q
Segment's industry liquidity
Divestment dummy
Fully divested segment dummy
0.090
0.013
0.008
-0.001
1.635
0.119
0.196
0.166
0.164
0.144
0.571
0.112
565
565
565
517
574
572
0.383
0.047
0.487
0.212
574
572
1.040
934.9
0.698
0.724
Sub sample of segments with divestments only
Number of divested business units
1.468
Aggregate transaction value of
divested business units ($-mln)
386.2
Aggr.transaction value/sales t-1
0.396
Aggr. transaction value/assets t-1
0.417
0.088
0.003
0.004
-0.005
1.704
0.122
0.197
0.100
0.098
0.096
0.578
0.114
231
231
231
216
234
234
0.081
0.011
0.010
-0.002
1.688
0.106
0.224
0.150
0.151
0.147
0.572
0.103
363
366
363
327
368
364
0.087
0.010
0.008
-0.002
1.665
0.116
0.205
0.150
0.149
0.137
0.573
0.110
1159
1162
1159
1060
1176
1170
0.346 0.477 234
0.039 0.193 233
0.411 0.493
0.057 0.233
370
367
0.385
0.049
0.487 1178
0.215 1172
220
1.185 0.422
81
1.214 0.647
152
1.332
0.844
126
125
126
481.8 965.6
0.510 0.854
0.479 0.723
46
46
46
318.1 632.2
0.466 0.757
0.492 0.740
88
88
88
380.1
0.440
0.454
849.3
0.746
0.727
0.066 **
0.009
0.004
-0.014 *
1.547 ***
0.124
0.206
0.130
0.131
0.150
0.485
0.113
809
815
809
747
817
812
0.383
0.051
0.486
0.220
823
822
453
1.360
0.922
315
260
259
260
322.0
0.558
0.540
940.4
0.891
0.786
193
193
192
Panel B: Divested versus retained segments
Aggregate familiarity dummy
Direct experience
Inside-industry experience
Outside-industry experience
Divested (1)
Mean
SD.
0.590
0.492
0.286
0.452
0.105
0.307
0.198
0.399
N
768
768
768
768
Retained (2)
Mean
SD.
0.588
0.492
0.287
0.453
0.124
0.330
0.177
0.382
N
1233
1233
1233
1233
Sales t-1
Sales t-1/ firm sales t-1
Size<10% dummy
Core segment
Cash flow t-1/sales t-2
Industry-adj. cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry-adj. capxt-1/sales t-2
2,671
0.349
0.143
0.298
0.178
0.065
0.013
0.008
767
768
768
768
764
760
764
760
1,953 **
0.276 ***
0.207 ***
0.221 ***
0.190
0.086 **
0.008
0.005
1231
1232
1232
1233
1213
1208
1213
1208
7,242
0.241
0.351
0.458
0.196
0.199
0.137
0.137
5,102
0.211
0.405
0.415
0.198
0.210
0.145
0.145
36
Cross-subsidization
Segment's Tobin's q
Segment's industry liquidity
-0.004
1.643
0.120
0.127
0.539
0.106
714
765
761
-0.009
1.600 *
0.118
0.152
0.543
0.114
1093
1228
1221
37
Table 3: Segment divestment binary logit regressions
This table presents the results of binary logit regressions that explain from which type of segments firms
choose to divest assets (Panel A, Regressions (1) to (5) and Panel B, Regressions (1) and (2)). The
dependent variable takes the value of one for divested segments and zero for retained segments.
Regressions (3) to (5) of Panel B report the results of Tobit regressions that explain the relative size of the
divestitures, measured as the total sum of transaction values of all divested business units to the beginning
of the year segment’s assets. Segments without divestitures get the value of zero. We exclude divesting
segment years of which no transaction value is available, and we winsorize this variable at the one and 99percentile.Regressions (1) to (3) of Panel A incorporates the full sample of 608 firm years. All the other
regressions use the sub sample of firm years with variation in familiarity. Variation in familiarity stands
for firm years in which the CEO’s familiarity varies across the firm’s segments. We measure familiarity
by means of the CEOs’ work experience, which we split into direct-experienceexperience, inside-industry
work experience, and outside-industry work experience. The specialist dummy equals one for CEOs that
have one direct-experience segment in their firm, zero otherwise. The generalist dummy equals one for
CEOs that are familiar with all the segments, but in different levels of familiarity, zero otherwise. The
dummy missing transaction value equals one for segment years with a divestiture for which the transaction
value is missing.All other variables are self-explanatory or defined more completely in Table 2. The
subscripts refer to the year relative to the year in which firms announce their divestment. We truncate
ratios at -1 and +1. All regressions include year dummies and dummies for the number of segments
(unreported). P-values appear in parentheses and are based on robust standard errors clustered by firm.
Panel A: Segment divestment
(1)
Aggregated familiarity dummy
Variation in familiarity
Variation in familiarity * aggr. familiarity dummy
Direct experience
Variation in familiarity * direct experience
Full sample
(2)
(3)
0.139
(0.125)
0.344 ***
0.345 ***
(0.004)
(0.004)
-0.461 ***
(0.009)
0.089
(0.340)
-0.497 **
(0.015)
Inside-industry experience
Variation in familiarity * inside-industry exp.
Outside-industry experience
Variation in familiarity * outside-industry exp.
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capx t-1/sales t-2
Variation in familiarity
sample
(4)
(5)
-0.313 *
(0.063)
-0.439 **
(0.027)
-0.238
(0.279)
-0.282
(0.191)
0.192
(0.143)
-0.387
(0.136)
-0.606 **
-0.632 **
-0.624 **
(0.037)
(0.028)
(0.030)
1.483 ***
1.407 ***
1.451 ***
(0.008)
(0.010)
(0.007)
-0.276
-0.302
-0.308
-0.177
(0.457)
-0.966 **
-0.958 **
(0.012)
(0.013)
1.438 **
1.505 **
(0.042)
(0.032)
-1.290 *** -1.301 ***
38
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
Liquidity
(0.498)
1.556
(0.458)
0.409
(0.330)
1.060 **
(0.029)
-0.058
(0.774)
0.304 ***
(0.004)
-0.098
(0.542)
0.189
(0.747)
(0.471)
1.577
(0.452)
0.469
(0.256)
1.100 **
(0.025)
-0.047
(0.818)
0.307 ***
(0.004)
-0.104
(0.517)
0.119
(0.841)
Number of observations
1,805
1,805
McFadden R-squared
4.18%
4.45%
* significant at 10%; ** significant at 5%, *** significant at 1%
(0.468)
1.616
(0.450)
0.474
(0.257)
1.125 **
(0.021)
-0.037
(0.858)
0.309 ***
(0.004)
-0.107
(0.510)
0.145
(0.806)
1,805
4.50%
(0.003)
6.463 **
(0.021)
0.925 **
(0.034)
1.707 ***
(0.007)
-0.062
(0.807)
0.262 *
(0.082)
0.122
(0.534)
1.202
(0.128)
1,049
5.26%
(0.003)
6.591 **
(0.020)
0.939 **
(0.031)
1.775 ***
(0.004)
-0.033
(0.897)
0.268 *
(0.073)
0.111
(0.572)
1.241
(0.117)
1,049
5.39%
39
Panel B: Alternate specifications of segment divestment
Likelihood to divest
(1)
(2)
Aggregated familiarity dummy
Direct experience
Specialist
Specialist * direct experience
-0.592 ***
(0.007)
0.136
(0.336)
0.299
(0.336)
Generalist
-0.262
(0.227)
-0.114
(0.655)
-0.059
(0.860)
0.050
(0.911)
-0.203
(0.563)
-0.171
(0.477)
-0.994 **
(0.011)
1.503 **
(0.033)
-1.362 ***
(0.003)
6.231 **
(0.026)
0.954 **
(0.029)
1.651 ***
(0.009)
-0.040
(0.878)
0.265 *
(0.075)
0.076
(0.697)
1.314 *
(0.091)
-0.953 **
(0.014)
1.501 **
(0.034)
-1.305 ***
(0.003)
6.553 **
(0.020)
0.938 **
(0.032)
1.773 ***
(0.005)
-0.034
(0.896)
0.268 *
(0.072)
0.113
(0.569)
1.254
(0.116)
Generalist * direct experience
Inside-industry experience
Outside-industry experience
-0.436 *
(0.059)
Transaction value to segment assets
(3)
(4)
(5)
-0.224 **
(0.017)
-0.227 ** -0.204 *
(0.035)
(0.052)
-0.214 *
(0.089)
-0.233
(0.122)
Missing transaction value
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capx t-1/sales t-2
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
Liquidity
Number of observations
1,049
McFadden R-squared
5.62%
* significant at 10%; ** significant at 5%, *** significant
at 1%
1,049
5.39%
-0.659 **
(0.011)
1.060 ***
(0.001)
-0.277
(0.613)
3.003 **
(0.014)
-0.192
(0.755)
0.639 **
(0.046)
-0.192
(0.204)
0.250 **
(0.011)
0.279 **
(0.033)
0.819 *
(0.076)
875
5.56%
-0.659 **
(0.011)
1.066 ***
(0.001)
-0.275
(0.620)
3.009 **
(0.016)
-0.192
(0.755)
0.646 **
(0.041)
-0.192
(0.208)
0.250 **
(0.011)
0.280 **
(0.033)
0.816 *
(0.080)
875
5.56%
-0.145
(0.260)
-0.210
(0.157)
0.781 ***
(0.000)
-0.712 ***
(0.005)
0.991 ***
(0.003)
-0.129
(0.821)
2.360 *
(0.073)
-0.252
(0.685)
0.482
(0.133)
-0.176
(0.252)
0.249 ***
(0.010)
0.289 **
(0.029)
0.729
(0.132)
875
8.09%
40
Table 4: Segment divestment logit regressions: subsamples split according to CEO tenure
This table presents the results of binary logit regressions that explain from which type of segments firms
choose to divest assets for the sub sample of firm years with variation in familiarity. The dependent
variable takes the value of one for segments with a divestiture and zero for fully retained segments.
Regression (1) contains firm years with CEOs that have tenure up to two years. Regression (2) contains
firm years with CEOs that have tenure of three or more years. We measure familiarity by means of the
CEOs’ work experience, which we split into direct experience, inside-industry experience, and outsideindustry experience. All other variables are self-explanatory or defined more completely in Table 2. The
subscripts refer to theyear relative to the year in which firms announce their divestment. We truncate
ratios at -1 and +1. All regressions include year dummies and dummies for the number of segments
(unreported). P-values appear in parentheses and are based on robust standard errors clustered by firm.
Direct experience
Inside-industry experience
Outside-industry experience
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
Liquidity
(1)
Newly hired
0.105
(0.775)
0.139
(0.720)
-0.049
(0.915)
-1.233 *
(0.063)
0.753
(0.647)
-1.851 **
(0.015)
3.522
(0.417)
0.068
(0.892)
2.341 **
(0.034)
-0.297
(0.515)
0.355
(0.268)
0.169
(0.649)
1.743
(0.167)
(2)
Longer tenured
-0.669 ***
(0.006)
-0.370
(0.190)
-0.186
(0.464)
-0.862 *
(0.078)
1.853 **
(0.021)
-1.655 **
(0.026)
7.703 **
(0.017)
1.967 **
(0.016)
1.588 *
(0.063)
0.104
(0.745)
0.117
(0.518)
0.068
(0.787)
1.210
(0.205)
Number of observations
360
689
McFadden R-squared
6.47%
7.02%
* significant at 10%; ** significant at 5%, *** significant at 1%
41
Table 5: CEO selection
Panel A presents the results of a binary logit regression that explains from which type of segments firms
choose to divest assets. Model (1) consists of the full sample of divesting firm years, Model (2) the sub
sample of divesting firm years with CEOs having tenure up to two years, and Model (3) the sub sample of
divesting firm years with externally hired CEOs having tenure up to two years. The dependent variable
takes the value of one for segments with a divestiture and zero for fully retained segments. Table 2 defines
all variables. The subscripts refer to the year relative to the year in which firms announce their divestment.
We truncate ratios at -1 and +1. All regressions include dummies for the number of segments
(unreported). P-values appear in parentheses and are based on robust standard errors clustered by firm.
Panel B presents the relation between the predicted probability of a segment to be divested, based on the
regressions in Panel A, for firm years prior to CEOs’appointment. The panel presents the mean, median
and standard deviationof this predicted probability per level of familiarity for the sub sample of firm years
with variation in familiarity. We measure familiarity by means of the CEOs’ work experience, which we
split into direct experience, inside-industry experience, and outside-industry experience.
Panel A: Segment divestment binary logit regression
(1)
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
Liquidity
Full sample
-0.598 **
(0.037)
1.386 ***
(0.008)
-0.279
(0.468)
1.789
(0.354)
0.405
(0.326)
1.058 **
(0.029)
-0.062
(0.755)
0.272 ***
(0.005)
-0.099
(0.531)
0.288
(0.576)
(2)
Newly hired
-1.083 **
(0.031)
0.436
(0.689)
-0.657
(0.220)
4.637
(0.182)
0.363
(0.338)
1.738 **
(0.049)
-0.262
(0.446)
0.390 **
(0.032)
0.071
(0.812)
0.341
(0.686)
Number of observations
1805
578
McFadden R-squared
4.01%
4.12%
* significant at 10%; ** significant at 5%, *** significant at 1%
(3)
Newly and externally
hired
-1.533
(0.137)
-1.622
(0.390)
-0.606
(0.501)
7.222
(0.454)
1.541
(0.407)
1.288
(0.451)
-0.497
(0.505)
0.787 **
(0.027)
-0.212
(0.725)
-0.081
(0.954)
180
9.16%
42
Panel B: Predicted probability to divest per type of familiarity
N
Direct exp. Inside ind. exp. Outside ind. exp. Non-familiar
(1)
(2)
(3)
(4)
91
54
32
119
Based on Model (1), the full sample
Mean
0.415
0.341
Median
0.400
0.331
SD.
0.104
0.069
P-value of differences
(1) - (4) (2) - (4) (3) - (4)
0.397
0.383
0.090
0.366
0.339
0.099
0.001
0.000
0.052
0.197
0.116
0.039
Based on Model (2), the sample with newly hired CEOs
Mean
0.411
0.348
0.402
Median
0.401
0.341
0.409
SD.
0.116
0.068
0.086
0.366
0.353
0.105
0.004
0.004
0.173
0.368
0.073
0.024
0.460
0.311
0.401
0.447
0.016
0.043
Based on Model (3), the sample with newly and externally hired CEOs
Mean
0.351
0.311
0.395
0.334
Median
0.371
0.332
0.415
0.334
SD.
0.160
0.158
0.110
0.169
43
Table 6: Segment divestment logit regressions
This table presents the results of binary logit regressions that explain from which type of segments firms
choose to divest assets for the sub sample of firm years with variation in familiarity. The dependent
variable takes the value of one for divested segments and zero for retained segments. We measure
familiarity by means of the CEOs’ work experience, which we split into direct experience, inside-industry
experience, and outside-industry experience. The high entrenchment dummy equals one for firm years in
which the entrenchment index as constructed by Bebchuk, Cohen and Ferrel (2009) equals three or higher.
The good internal governance dummy equals one for firm years where the percentage of inside directors is
above the median of our sample. All other variables are self-explanatory or defined more completely in
Table 2. The subscripts refer to the year relative to the year in which firms announce their divestment. We
truncate ratios at -1 and +1. All regressions include year dummies and dummies for the number of
segments (unreported). P-values appear in parentheses and are based on robust standard errors clustered
by firm.
Direct experience
Direct experience * high entrenchment dummy
(1)
-0.545 **
(0.047)
0.106
(2)
-0.513 *
(0.054)
(0.727)
High entrenchment dummy
-0.120
(0.398)
Direct experience * good internal governance dummy
0.021
(0.943)
-0.119
(0.337)
Good internal governance
Inside-industry experience
Outside-industry experience
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
-0.238
(0.283)
-0.159
(0.514)
-0.932 **
(0.018)
-0.200
(0.371)
-0.150
(0.550)
-1.015 **
(0.013)
1.621 **
1.632 **
(0.021)
(0.028)
-1.354 ***
(0.003)
6.592 **
(0.021)
1.029 **
(0.023)
-1.348 ***
(0.002)
7.274 **
(0.011)
0.995 **
(0.021)
1.753 ***
1.973 ***
(0.006)
(0.003)
0.004
(0.989)
0.28 *
(0.061)
0.079
(0.689)
-0.045
(0.865)
0.268 *
(0.078)
0.081
(0.683)
44
Liquidity
Number of observations
McFadden R-squared
* significant at 10%; ** significant at 5%, *** significant at 1%
1.365 *
(0.097)
1.401 *
(0.097)
1023
5.36%
1004
5.74%
45
Table 7: Segment divestment logit regressions
This table presents the results of binary logit regressions that explain from which type of segments firms
choose to divest assets. The dependent variable takes the value of one for divested segments and zero for
retained segments. Panel A only incorporates direct-experience segments of the sub sample of firm years
with variation in familiarity and Panel B incorporates all segments of firm years with variation in
familiarity.We measure familiarity by means of the CEOs’ work experience, which we split into direct
experience, inside-industry experience, and outside-industry experience. All other variables are selfexplanatory or defined more completely in Table 2. The subscripts refer to the year relative to the year in
which firms announce their divestment. We truncate ratios at -1 and +1. All regressions in Panel B include
year dummies and dummies for the number of segments (unreported). P-values appear in parentheses and
are based on robust standard errors clustered by firm.
Panel A:Sample of direct-experience segments
Direct experience, 4 - 7 years work experience
Direct experience, > 7 years work experience
(1)
-0.151
(0.663)
0.062
(0.862)
Direct experience, left 5-9 years ago
(2)
-0.163
(0.608)
-0.269
(0.318)
Direct experience, left > 9 years ago
Direct experience, 2-4 yrs corporate experience
Direct experience, >4 yrs corporate experience
Constant
(3)
-0.445
(0.102)
Number of observations
323
McFadden R-squared
0.14%
* significant at 10%; ** significant at 5%, *** significant at 1%
-0.384 **
(0.029)
-0.605 **
(0.035)
0.061
(0.808)
-0.336 **
(0.048)
317
0.23%
317
1.57%
(2)
(3)
Panel B: Full sample
Direct experience, < 4 years work experience
Direct experience, 4 - 7 years work experience
Direct experience, > 7 years work experience
Direct experience, left < 5 years ago
Direct experience, left 5-9 years ago
Direct experience, left > 9 years ago
Direct experience, < 2 yrs corporate experience
(1)
-0.415
(0.152)
-0.519 **
(0.023)
-0.384
(0.154)
-0.382 *
(0.088)
-0.465
(0.128)
-0.630 ***
(0.006)
-0.386 *
46
Direct experience, 2-4 yrs corporate experience
Direct experience, >4 yrs corporate experience
Inside-industry experience
Outside-industry experience
Cash flow t-1/sales t-2
Industry median cash flow t-1/sales t-2
Capxt-1/sales t-2
Industry median capxt-1/sales t-2
Cross-subsidization
Sales t-1/ firm sales t-2
Core segment
Segment's Tobin's q
Size<10% dummy
Liquidity
-0.240
(0.274)
-0.177
(0.456)
-0.952 **
(0.014)
1.467 **
(0.040)
-1.301 ***
(0.003)
6.651 **
(0.018)
0.934 **
(0.033)
1.765 ***
(0.005)
-0.036
(0.891)
0.265 *
(0.075)
0.110
(0.579)
1.237
(0.118)
Number of observations
1049
McFadden R-squared
5.40%
* significant at 10%; ** significant at 5%, *** significant at 1%
-0.260
(0.231)
-0.192
(0.415)
-0.987 **
(0.011)
1.494 **
(0.032)
-1.278 ***
(0.004)
3.345
(0.283)
0.831 *
(0.068)
1.869 ***
(0.002)
-0.027
(0.917)
0.265 *
(0.076)
0.160
(0.414)
1.086
(0.173)
1043
5.11%
(0.083)
-0.913 ***
(0.000)
-0.332
(0.168)
-0.289
(0.186)
-0.240
(0.305)
-1.081 ***
(0.005)
1.456 **
(0.039)
-1.375 ***
(0.001)
3.781
(0.210)
1.033 **
(0.020)
1.917 ***
(0.002)
-0.013
(0.961)
0.245
(0.106)
0.159
(0.425)
0.918
(0.260)
1037
5.70%
47
Table 8: Analysis of CARs to divestiture announcements
Panel A presents the means, standard deviations, and mean differences of the cumulative abnormal returns
over days -1 to +1 relative to the divestiture announcement. We estimate the abnormal returns by means of
the market model with an estimation window running from day -160 to day -41 relative to the
announcement date. The abnormal returns are winsorized at the one- and 99-percentile values.We measure
familiarity by means of the CEOs’ work experience, which we split into direct experience, inside-industry
experience, and outside-industry experience. Newly-hired CEOs are CEOs with tenure up to two years
and longer-tenured CEOs are CEOs with tenure of three or more years. Panel B presents the results of
ordinary least squares regressions of three-day CARs to divestiture announcements. The relative
transaction size is the transaction value divided by the book value of the firm’s total assets. We calculate
the firm’s industry adjusted capital expenditures, leverage, and cash as the firm variable minus the median
of all Compustat firms with the same two-digit SIC code. All other variables are defined in Tables1 and 2.
P-values appear in parentheses and are based on Huber-White standard errors.
Panel A: CARs in familiarity and tenure subsamples
Full sample
Sample with available
information
Mean
SD.
N
Direct exp. Inside ind. exp. Outside ind. exp. Non-familiar
(1)
(2)
(3)
(4)
0.42%
* 0.39%
0.69% **
0.74% ***
(4.30%)
(4.64%)
(4.19%)
(3.97%)
286
84
166
373
*
Mean
SD.
N
0.55%
(4.64%)
122
0.27%
(4.64%)
40
0.84%
(4.63%)
81
0.79% *
(4.35%)
177
Full sample
Mean
SD.
N
0.51%
(4.25%)
193
* 0.76%
(4.63%)
46
0.40%
(4.05%)
90
0.56% *
(3.95%)
278
Sample with available
information
Mean
SD.
N
0.70%
(4.50%)
80
0.42%
(3.49%)
23
0.51%
(3.93%)
46
0.50%
(4.23%)
142
Mean
SD.
N
0.23%
(4.43%)
93
-0.06%
(4.68%)
38
1.04% **
(4.36%)
76
1.28% **
(4.00%)
95
Mean
SD.
N
0.28%
(4.94%)
42
0.06%
(5.96%)
17
1.28%
(5.44%)
35
1.96% *
(4.70%)
35
Longer-tenured CEOs
*
Newly-hired CEOs
*
Full sample
Sample with available
information
*
48
Table 8: Analysis of CARs to divestiture announcements (cont.)
Panel B: Regression analysis explaining CARs
Direct experience
(1)
0.003
(0.512)
(2)
0.002
(0.780)
(3)
0.010
(0.114)
Direct experience, long tenured
Direct experience, newly hired
Excess value
-0.001
(0.822)
0.004
(0.417)
Core segment
Internally hired CEO
Founders
Inside-industry experience
Outside-industry experience
Relative transaction size
Tobin's q
Industry-adj. capxt-1/sales t-2
Industry-adj. leverage
Industry-adj, cash t-1/sales t-2
Percentage of stock owned
Intercept
-0.001
(0.857)
-0.000
(0.966)
0.102 ***
(0.001)
-0.006 ***
(0.002)
0.130
(0.107)
0.020
(0.149)
0.008
(0.727)
-0.038
(0.313)
0.010 *
(0.051)
-0.002
(0.773)
-0.003
(0.653)
0.106 ***
(0.001)
-0.006 ***
(0.005)
0.149
(0.127)
0.021
(0.140)
0.006
(0.838)
-0.042
(0.282)
0.010 *
(0.054)
Number of observations
420
386
Adjusted R-squared
6.30%
6.20%
* significant at 10%; ** significant at 5%, *** significant at
1%
-0.001
(0.931)
0.003
(0.528)
-0.015 **
(0.039)
-0.028 **
(0.027)
0.001
(0.872)
-0.006
(0.373)
0.107 ***
(0.001)
-0.007 ***
(0.004)
0.143
(0.146)
0.018
(0.229)
-0.002
(0.956)
-0.028
(0.477)
0.021 ***
(0.005)
386
7.50%
(4)
0.016 *
(0.051)
0.003
(0.669)
-0.001
(0.832)
0.002
(0.710)
-0.014 **
(0.041)
-0.032 **
(0.021)
0.001
(0.868)
-0.006
(0.402)
0.106 ***
(0.001)
-0.007 ***
(0.005)
0.148
(0.137)
0.018
(0.233)
0.005
(0.858)
-0.030
(0.440)
0.022 ***
(0.005)
386
7.70%
49