Is it ever prudent to form a global
conglomerate? An industry specific
investigation.
Author:
Garrett C. Smith
Abstract:
In spite of the vast amount of literature covering diversification, as well as the effect, both in an
industrial and international setting there remains an area left under investigated. Namely, is the
effect whether value enhancing or destroying uniform across different industry groups? Prior
literature typically assumes the effect (positive or negative) to be uniformly distributed. Using
panel data covering a 30 year period (1982-2011) it is found this effect is not homogenous.
Twenty-seven portfolios were constructed following Fama and French’s thirty portfolio
specifications, to investigate the industrial effects. First, the sample was used under a pooled
ordinary least squares (OLS) framework showing that different industries respond to the different
diversification possibilities differently. The results still exist after controlling for “self-selection”
bias using Heckman’s Two Stage regression framework. Lastly, a quantile regression technique
was also employed to test for the existence of this non-uniform response using both enterprise
value and return on assets (ROA).
1. Introduction / Motivation
Over the last fifty years the perceptive value of diversification has been debated in both the
board room and in extant literature. Ultimately, the perceived value to be gained from
diversification seemed to depend upon the year of the calendar, and covered three possible
outcomes: value enhancing, value destroying or of no effect. Diversification in general can be
performed broadly in three channels: industrial diversification, global diversification
(internationalization), or both. Because the ramifications of diversification have significant
impact upon the firm, the literature on this topic can be found in a number of different business
research areas including strategic management, international business and finance.
Much of the literature for this topic began to be developed in the 1970s and 1980s. Much of
this theoretical work formed the underpinnings for the empirical work that began in earnest in
the 1990s. Lang and Stulz (1994), Berger and Ofek (1995) and Servaes (1996) all contributed
empirical studies pointing toward a diversification discount in industrially diversified firms.
This view point was so accepted that finance textbooks as well as consulting companies began to
espouse this view as fact. However, over the last ten years this position is once again being
debated. A number of researchers claim that the apparent diversification discount is an artifact
the result of the data used, endogeneity bias, or other biases within the Berger and Ofek (1995)
methodology (see for example Campa and Kedia (2002), Villalonga (2004a, 2004b)).
Contemporary literature is still split, for example Rudolph and Schwetzler (2012) as well as
Ammann et al (2012) find a discount after correction while Lee and Li (2012) report
diversification is heteroskedastic, but the actual realized effect from diversification is not
significant once firm risk is introduced as a control variable. Finally, Creal et al. (2012) report
that global diversification creates a significant diversification premium for US firms.
The literature has only been concerned whether an effect from diversification exists on
average. The bulk of the finance literature, deals with industrial diversification, while a
comparatively smaller sample deals with global diversification. While in some fashion or
another most of the literature acknowledges that industry effects could impact the outcome of
diversification it is not investigated extensively. This is because either implicitly or explicitly
these papers assume that the effect(s) would be heterogeneous across industries. However, for
industrial diversification, Santalo and Becerra (2008) investigate how the characteristics of the
industry in which a firm operates can affect the outcome of the diversification decision.
Subsequently, they report industry effects are not homogenous. In particular how diversified the
firm’s competitors are within a given industry have a significant impact upon whether the firm is
valued at a premium, discount or has no significant change when compared to the pure play
companies.
Denis et al (2002) suggest that industrial and global diversification may be substitutes for
each other. In this paper the authors also report that industry and time may be a factor,
contributing to any observed effect. Their findings suggest that both industrial and global
diversification are value destroying undertakings. Subsequently, firms that were both
industrially and globally diversified fared worse than being diversified in only one of the two
categories.
Since there is still a debate within the literature as to the effects of diversification, a larger
sample analysis of industry effects as it relates to the diversification choice is warranted. A thirty
year time period (1982-2011) is used for this study. Since this time period covers recovery from
two major recessions as well as several major boom and bust cycles this may help to determine if
time (or timing) plays any roll in observed results. Additionally, since the time frame overlaps
with previous studies there is the ability to compare the results in and out of a larger sample to
that of previous research.
The lack of specific research upon industry effects in global diversification (as well as those
firms which are jointly diversified) provides motivation for the research. Management faces a
choice to diversify, and they can choose how to undertake this action. As such, the literature
states this creates endogenous effects in the empirical analysis. When faced with the option to
diversify industrially, globally or both the choice is more complex than just choosing between
one branch on the decision tree (industrially or globally) (Gande et al 2009).
I argue that because of the increased complexity of the choice that a firm’s management
faces, industry specific factors will play a significant role in this process. As such, similar to the
findings presented by Santalo and Becerra, I predict that the value premium (discount) observed
in the three choice paths will depend upon the industry in which the firm operates. The remainder
of the paper is organized as follows; section two reviews the literature relating to the topic,
section three describes data collection, section four methodology, five results and discussion and
six the conclusion.
The findings of the paper refute the idea that the effect of diversification is homogenous
across industries. After, using an ordinary least squares (OLS) framework, the Heckman Two
Stage regression approach and quantile regression techniques (QR) it is noted that the response is
not uniform across industries. When correcting for possible self-selection bias the response
within industry is not uniform, where on average the response is to correct is a positive shift to
the right, when investigating by industry findings it is seen that the response is not uniform, some
shift to the right while others move to the left. Furthermore, the response is not uniform within
industry. These responses seem to be linked to both the competitiveness of the industry as well
as barriers to entry.
2. Related Literature
This section is to serve as an overview of the most important theoretical and empirical
studies pertaining to the topic. For a more comprehensive review of the industrial diversification
literature it is suggested to refer to Martin and Sayrak (2003), Benito-Osorio et al. (2012) and
Erdorf et al (2012). Li (2007) as well as Eckert and Trautnitz (2010) provide a basis for review
on international diversification. As a note for implications of global diversification, as it relates
to finance, one may wish to refer to the cross-border merger and acquisition (M&A) as well as
foreign direct investment (FDI) literature.
A) Theoretical Framework
Broadly there are several reasons why diversification may be beneficial or detrimental to a
firm. Most of the detrimental effects are a result of agency and internal governance costs. For
instance, Jones and Hill (1988), theorize that as a firm globalizes, its transaction and
coordination costs increase. Additionally, costs which could apply to both industrialization and
internationalization include information asymmetry (Harris et al. (1982)), incentive
misalignment between headquarters and divisional or subsidiary management (Roth and
O’Donnell (1996)), and the subsidization of underperforming business segments by those
segments which are profitable (Rajan et al (2000) and Scharfstein and Stein (2000). After
modeling the inefficient internal capital markets Rajan et al. support their theory with empirical
analysis. Managers may also gain personal benefits at the cost of shareholders through unneeded
diversification; including, but not limited to, increased pay and prestige or through entrenchment
by the management (Jensen (1986)), Shleifer and Vishny (1989), and Jensen and Murphy
(1990)). If a manager’s wealth is highly concentrated and connected to that of the firm Amihud
and Lev (1981) theorize that managers may diversify the company to reduce their own personal
financial risk.
Aggarwal and Samwick (2003) further this line of thought by developing a contracting model
in an attempt to identify if managers diversify their firms due to the agency issues outlined
above. Namely, does the attempt to gain personal incentives (compensation or prestige) motivate
managers to diversify or is it to lower firm risk drive the incentive to diversify? After forming
the theory they empirically test their model and conclude that managers diversify because of
personal incentives.
Lyandres (2007), first develops and then empirically tests a model which illustrates how
inefficient capital structures within divisions because of organization structure can lead to a
reduction in firm value. The model is based upon the contingent claims framework . Another,
theoretical model suggests that apparent decrease in firm value is actually consistent with a firm
acting to maximize shareholder value, due primarily to a decline in productivity in the original or
main line of the business. This model, developed by Gomes and Livdan (2004), yields results
which are consistent with empirical papers such as Campa and Kedia (2002) which suggest that
it is undervalued companies which diversify.
On the other side of the debate potential benefits to diversification could arise from taking
advantage of economies of scope or scale from operating in multiple industries or nations (Teece
(1980) and Caves (1996)). Additionally, the value of diversification could be greater for firms
that have unique assets within the firm (Caves 1971). These assets are typically intangible or
information based in nature, such as management skill or superior manufacture technique and
exploitation would lead to abnormal returns (Buckley 1988).
Because of comparable advantages in a different country as opposed to the home nation a
multi-national corporations (MNCs) may be able to exploit these differences across nations and
be more competitive in both markets (Kogut and Chang (1991)). A MNC may also be able to
exploit differences in tax structures across nations, this profit shifting action could generate
excess value for the MNC compared to local (confined) businesses (Errunza and Senbet (1984)
and Bartelsman and Beetsma, (2003)).
Exploitation of internal capital markets may allow a firm to invest in positive net present
value projects that a pure play company may not be able to finance due to capital constraints. It
is theorized that internal capital markets have a comparative advantage over external markets
(Scharfstein and Stein (2003) and Stein (1997)). A final benefit to be considered here is that
diversification ceteris paribus should decrease the volatility of firm’s cash flows, providing the
business operations are not perfectly correlated (Lewellen, 1971). Lewellen, also suggests that
this action should increase the firm’s debt capacity.
B) Empirical Studies
As commented upon in the introduction, three major studies in the 1990s have findings that
imply diversification destroys value (Lang and Stultz (1994), Berger and Ofek (1995) and
Servaes (1996)). Denis et al. (1997), report findings that imply agency issues are in large part
responsible for firms continuing value reducing strategies in spite of the empirical evidence that
companies should not diversify. The methodology employed by Berger and Ofek became the
standard for investigation of diversification discount to present. Denis et al. (2002), report that
on average the costs outweigh the benefits of diversification. If one continues to look at the
more current literature supporting a value destructive position the following have important
implications.
Fauver et al (2003) was one of the first papers to explore the effects of diversification on nonUS firms. Through their analysis they found that effects on capital markets can change the
outcome of diversification. For firms with high capital market integration and strong legal
systems, firms trade at a discount, this supported earlier findings reported by Lins and Servaes
(1999). However, for firms operating in less developed markets there is no discount and in some
instances a premium was found. Also reported is the discount from diversification is affected by
the La Porta et al. (1997) framework.
In more recent literature a discount is still noted, for instance, Kim and Mathur (2008), report
similar findings to those of Denis et al. 2002, namely that both types of diversification are value
destroying. In that paper the authors also report an apparent link between the two modes of
diversification. Additionally, evidence supporting the agency costs theory is presented; it is
found on average firms, with higher manager based equity compensation, are associated with
higher firm value. This would imply that tighter internal corporate governance may reduce the
apparent discount from diversification.
Since the methodology as outlined by Berger and Ofek, cannot handle financial firms,
Laeven and Levine (2007), use slightly different methodology, but also report a diversification
discount for financial firms. Ferris et al. (2010) report for a sample of firms from 35 different
countries that firms which are only globally diversified have a positive but insignificant
diversification effect. However, for industrially and both industrially and globally diversified
firms a significant reduction in firm value was found.
As documented in the theoretical literature there are a number of ways in which
diversification could be beneficial or detrimental. While internal capital markets could give
diversified firms an advantage through size or scope (Caves, 1996) it is possible that inefficient
investment within this market could also be value destroying (Rajan et al. 2000).
All of the empirical research conducted over the years showing a negative relationship could
lead a rational observer to question, in the light of all the negative evidence, why companies still
pursue diversification strategies? This basic question has motivated many papers to explain the
somewhat paradoxical results. Adding to this quandary is the number of empirical studies in the
FDI space in particular that show that MNCs from developed economies should have distinct
advantages local firms. This especially holds true when comparing the subsidiary of the MNC
with local firms that solely operate in highly segmented capital markets or in countries with
increased political risk (see for example Desai et al. (2004, 2006 and 2008) .
Many of the empirical studies which attempt to rectify this situation call into question either
the database, methodology, econometrics, or a combination of the three used in the papers which
come to value destroying diversification conclusions. After correcting for whatever specific
issue including measurement and endogeneity errors, lack of control variables or methodology,
(debt holdings, cash holdings) or methodological (incorrect SIC classifications or measurement
of pure play value) the author typically report a value premium or at minimum no effect from
diversification.1 In what appears to be typical for this type of research recently some authors
report even after correcting for the issues a discount is still observed.2
When considering the possible errors in the methodology which leads to the presence of a
reported discount for diversification the largest amount of literature deals with endogeneity.
Management has the ability to choose its diversification path. It is also possible that the
1 Refer to Campa and Kedia (2002), Villalonga (2004a, 2004b), Mansi and Reeb (2002), Kumar 2009, Dastidar (2009), and Graham et al.
(2002) to name a few of the core papers
2 Refer to Dos Santos et al. (2008), Glaser and Muller (2010), Ammann et al. (2012), Borghesi et al. (2007), Hoechle (2012) and Rudolph and
Schwetzler (2012)
operating environment or other factors that would influence the choice to diversify also
endogenously affect the value of the firm. As such, how should diversification be modeled in
order to determine if an effect (premium or discount) exists? The three most influential papers
that first explored this question were Campa and Kedia (2002), Graham et al. (2002), and
Villalonga (2004b). In using the standard methodology it is assumed that the standalone pure
play companies are accurate proxies for the divisions or subsidiaries of the diversified firm.
Graham et al. show empirically through their sample that this may not be a valid assumption.
They argue that since managers can select the firms they wish to acquire and that these firms are
already discounted on average prior to the acquisition. Since, the acquired firms have a
discounted value prior to acquisition compared to pure play companies, pure play companies are
not an appropriate proxy for divisions or subsidiaries. Therefore, one may conclude
diversification in and of itself does not destroy value.
Using similar logic and methodologies both Campa and Kedia (2002) and Villalonga (2004b)
correct for possible bias. The conclusions were similar in both papers. In Campa and Kedia it is
reported that when controlling for endogenous effects using either an instrumental variable
approach or Heckman’s two stage regression the value of diversifying is in fact positive. While
Villalonga, who presents two additional econometric techniques in addition to Heckman’s two
stage estimation process, report that the effect is statistically insignificant after correction.
Villalonga argues that using a cross sectional approach as in Campa and Kedia results in a
measurement of diversity discount and not diversification discount. Using this same logic she
refutes the results of Lamont and Polk (2002).
Both of these papers were only concerned with industrial diversification. Dastidar (2009),
follows the methodology of Campa and Kedia and applies the two stage estimation process.
After correction he report similar results to those documented for industrial diversification, more
specifically a diversification premium as opposed to discount. Gande et al 2009 and Kumar
2009 both explore how this potential endogenous effect may influence whether a firm selects to
expand industrially or internationally. The outcome supports an easy extension of logic that if
industrial or global diversifications are essentially substitutes then in the short run these effects
would be negatively related. As such the choice could be even more complex than examining
either of them individually. This stands to reason as diversification by any of the paths will
require capital, and the firm will likely have capital constraints in the short run which would
prevent them from pursuing all possible positive net present value (NPV) projects.
Recently, however several studies have applied the methodology to correct for endogenous
effects and still find both an economically and statistically significant negative results. The
literature that finds a discount usually links the discount to agency costs overcoming the benefits
of diversification. The agency costs are all closely tied to corporate governance issues. With this
in mind Hoecle at el. (2012) conducted research to test how the diversification discount is tied to
in some fashion corporate governance. In their research they find that using both dynamic panel
modeling and Heckman’s method that diversification even after accounting for endogeneity leads
to a discount. In the paper they also report that if corporate governance variables are introduced
to the regressions the apparent discount is reduced, but still significant. They propose that this
supports the idea that the discount is tied to corporate governance and/or agency cost affects. In
their paper Ammann et al (2012) show that if firm fixed effects are present within samples used
to compute the diversification discount, but ignored than the discount may appear to disappear.
Using the model criterion in Campa and Kedia while allowing for fixed effects in the second
stage of the regression, the authors report that a discount is still present.
Connected to corporate governance literature how firms use internal capital markets arising
from diversification is also an issue which deserves to be investigated. This includes but is not
limited to how firms use and access capital under times of constraint. There is a large amount of
literature which deals with internal capital markets and their relation to capital constraints3, but
few deal directly with the relationship. Ahn et al (2006) report that on average diversified firms
allocated funds less effectively than firms that are pure play companies. Closely related to the
study by Ahn et al., Ozbas and Scharfstein (2010) empirically test for what they call the dark side
of internal capital markets. The authors report that the investments made by diversified firms
tend to occur more often in lower Tobin’s Q related industries. It is possible that these choices
are what drive the observed diversification discount. They also find evidence that agency issues
could be a significant contributor to the observed poor internal capital flows and investment
choices in diversified firms. Using a proprietary data set Duchin and Sosyura (2012) report that
social connections play a role in internal capital flows. The results can be mixed, but on average
those divisional managers with social connections to the CEO are allocated more capital within
the framework of the company. Additionally, they report the connection to corporate governance
stating that in a weak environment these connections reduce investment efficiency and firm
value.
Several studies investigate diversification and how internal capital markets are used under
times of capital constraint. These include Yan et al. (2010), Kuppuseamy and Villalonga (2010)
and Volkov (2012). Both Yan et al. and Kuppuseamy and Villalonga studies show that under
capital constraint conditions being diversified is advantageous. They both additionally report
that allocation of capital is optimal and generally increase firm value.
3 For a list of related literature refer to Kuppuseamy and Villalonga (2010)
One may wish to question the measurement accuracy or quality of reporting contained within
the dataset being used for the investigations. A large portion of the studies including this one use
the COMPUSTAT database to obtain the raw data used for the investigation of the relationship
between diversification and firm value. Villalonga (2004a), suggests that this dataset is suspect,
and explores the possible effects as it relates to industrial diversification. Using a unique dataset
she report that a premium for diversification exists as opposed to a discount. Another possible
error could be an error in variables. Hoecle et al. (2012) also report in their study that there
seems to be significant changes in the data of the COMPUSTAT data base in the time that past
between their study and Villalonga and some of the other studies that question its validity.
Whited (2001) explores this possibility and suggests that measurement error is in fact the
main source for the value reducing effects observed. For this investigation his value
measurement was Tobin’s Q, related to the firm value measurement from Berger and Ofek.
Gande et al. (2009) using similar methodology as Whited reports positive Q values for global
diversification while no effect for industrial diversification. Conversely, Lamont and Polk
(2002) acknowledge that measurement error could lead to spurious regression results, but they
argue that specifications can be selected which provide accurate results.
It stands to reason that firms should be able to reduce their risk through diversification. This
holds true regardless of which type of diversification path is chosen, but may be strongest for
those firms which have diversified both internationally and industrially. This reduction in risk
could allow for Lewellen’s (1971) theory of debt co-insurance to hold. In a contingent claims
framework this risk shift should benefit bondholders and come at the expense of shareholders.
This may imply that the actual value of the firm has not decreased, but the firm value
measurement which is skewed toward equity holders should in fact be negative. Mansi and Reeb
(2002) first formally proposed this idea and tested empirically for industrial diversification.
Doukas and Kan (2006) investigated this idea for global diversification. In both articles the
authors conclude that a diversification discount does not exist. Glaser and Muller (2010) draw
upon the framework proposed by Mansi and Reeb as well as the estimation procedure for the
market value of debt proposed by Merton (1974)4 for German companies. They find that the
effect exists and correction reduces the valuation discount, but the discount remains. Ammann et
al. (2012) using similar methodology also report finding a relationship between leverage and the
discount. Firms with zero leverage do not appear to have a discount, but the diversification
discount is present and increases with increasing firm leverage. They conclude that their results
support what they call the value transfer hypothesis, as outlined above.
Is it possible that diversified firms differ in ways from the pure play companies that is not
controlled for in the current methodology? The overall characteristics of a diversified firm and
especially a jointly diversified firm (global and industrial) have different characteristics from
those of the pure play companies which serve as the proxy for the standalone value of the
subsidiaries or divisions in a diversified company by the Berger and Ofek methodology. A
number of control variables are used (see Denis et al. (2002)), but some researchers have
proposed that key differences have been overlooked.
Duchin (2010) studies the connection between cash holdings and liquidity as it relates to
diversification in an industrial setting. Using these finds Rudolph and Schwetzler (2012) make
adjustments for the disparity in cash holdings between diversified and non-diversified firms.
Investigating industrial diversification with international data, they report that a bias resulting
from the difference in cash holdings can be observed. However, for mature economies (US, UK
4
More specifically Glaer and Muller use the methods proposed by Bharath and Shumway (2008), Eberhart (2005) and Vassalou and Xing
(2004) to estimate Merton’s model, and derive the market value of debt, when the market value of debt is not directly observable.
and Japan) a discount persists even after correction. For the other countries in their sample (a
total of eighteen) they report insignificant outcomes after correction.
Since firms that are generally in a position to diversify are typically older more established
firms, it is possible that firm age could play a role in the apparent diversification discounts. In
the literature this variable between firms was not explored initially. After noting this fact
Borghesi et al. (2007), report a smaller (roughly 8%), but still significant reduction to firm value
for industrial diversification, after controlling for firm age. The authors note that the discount is
approximately half the size as reported by Denis et al. in 2002. Similar results would be
expected for international diversification.
3. Data Collection / Methodology
The Berger and Ofek methodologies were used with small modifications based on more
recent studies, which will be discussed as they arise. Accounting data for the firms were
collected from the Compustat database. This data was collected for the entire database for the
years 1982-2011, giving a thirty year sample. This initial raw sample contained 316,623 firm
years. From the historical segment files in Compustat, segment data was collected covering the
same time period, yielding a total of approximately 1.5 million firm segment observations (for a
detailed account of how firms report their segments refer to Denis et al,2002 or Gande et al,
2009). Data collected to compute market value is from CRSP database. Data for the real US
GDP was collected from the BEA website.
During the sample period the accounting rules affecting how the segments are reported
changed. This change occurred during 1997, which is approximately halfway through the
sample period under investigation. In the earlier years of the sample (1982-1997) the reporting
rule governing segments was SFAS 14. Under this accounting rule firms were required to report
business units by an approach consistent with line of business. Under this requirement to be in
compliance companies had to report information by both industry and by geographic area. In the
later portion of the study (1998-2011), the rule governing segmental reporting became SFAS
131. Under this rule firms are required to follow a management approach. Meaning, that
reporting of the segments by a firm must follow the same structure that company management
uses. In spite of the change in rules, it is felt that spanning this time period is acceptable and
should not be materially affected by the change in accounting standards. This is based upon
previous findings of no material differences between a pre and post regime change (see for
example Gande et al., 2009 and Ammann et al., 2012).5
Following the accepted methodologies of Denis et al. (2002) observations were removed
from the sample set for the following reasons: the firm is a non-US firm, sales of a firm are less
than $20M, segment sales are reported as either negative or zero, summation of the reported
segment sales are not within one percent of total reported firm revenue, financial and utility
companies (SIC codes 4900-4999 and 6000-6999), and any firms with insufficient financial
information needed for the study. This process left a total of 41,078 firm year observations.
Following Rudolph and Schwetzler (2012), enterprise value was computed as opposed to
the Berger and Ofek’s (1995) firm value measure. This however, is only a small modification.
The firm value measure (value of total capital) which is calculated as market value equity plus
the book value of debt. To compute the enterprise value, from the firm’s total capital cash and
equivalents are subtracted out of this figure. This was done in order to remove the bias of nondiversified firms carrying more cash than those of diversified firms. Once the enterprise value
5
The regressions were also run to test for the effect within this sample and the subsequent results were not
materially different, the results are not reported.
for all firms was calculated all pure play US firms were identified from the final 41,078 firm
years.
Next, to compute the imputed value of a diversified firm as a sum of its segments the
enterprise value of the pure play companies was divided by its sales. Santalo and Becerra
(2008), suggest that in the Berger and Ofek specification of using five firms or more firms by
SIC four digit code, then by three and finally by two as needed to find at least five pure play
companies is arbitrary and reduces diversification artificially. This is done to find a reliable
median value, which was found to be a better estimate than the arithmetical average.
Santalo
and Becerra suggest matching by four digit SIC regardless of the number of pure plays in the
segment, and using the median value. Rudolph and Schwetzler illustrate how median values can
skew the results of the tests. They suggest that a better estimate is the geometric mean. For the
paper both four digit SIC medians and geometric mean firm value to sales ratios were calculated
for all of the pure play companies.
To calculate the implied value of the segments of a diversified firm, a particular segment
is matched by four digit SIC code to that of the corresponding pure play median or geometric
means of the firm value to sales of corresponding four digit SIC codes. Once this ratio is
matched the sales figures of the segment is multiplied by the geometric mean (median) to yield
the implied value of the segment. Finally, to calculate the implied value of the firm for a given
fiscal year, summation of the entire segment values yield the implied value of the firm.
While only US firms are considered as pure play companies for this investigation of (both
global and industrial) diversification this approach is acceptable. However, as pointed out in
Creal et al. (2012), there is a distinct difference between using US pure play companies or using
matched foreign pure play companies to the international segments of the US firms. The first
method, as followed by the paper, assumes that the segment is repatriated to the US (and makes
assumptions that the market would be able to support the additional output). Where Creal et al.
through access to a propriety data set identify the countries in which the business segments
operate. After the countries of operation are identified, international data is used to calculate the
value of the foreign pure plays. In their study they report finding positive outcome from
international diversification. However, as already noted, they do not investigate the possibility
of industrial effects.
To explore the effect of the diversification discount many dummy variables were formed.
For the first level of dummies represents if a firm reported themselves in the Compustat data as
having international operations it was coded as a geographically diverse company. If any
company had more than one segment in a given year it was coded as an industrially diversified
firm. Lastly, if the firm received a one for both of the previous dummies it was coded as both
industrially and globally diversified, and the other dummies were set to zero. For the industry
dummies a modified form of the Fama and French thirty industry portfolios was used. From
French’s website the four digit SIC specifications were followed to build twenty-seven
portfolios. This corresponds to the thirty industry portfolios, less the financial and utility
industries. Due to sample size the “Beer” and “Smoke” portfolios were combined for the
purposes of the paper as “Vice”. Finally, interaction terms were constructed for all twenty-seven
portfolios and the three possible diversification dummies yielding a total of 81 dummies. It is
important to note that in all specification and under all of the various empirical investigations the
base value is always the pure play companies by industry. This holds for the large full sample
regressions as well as the smaller industry specific regressions.
The following are the testable hypotheses which are tested empirically for the paper.
H1: The observed diversification effect (premium, discount or zero) will differ across
industry specification.

Tested using an OLS framework with the dummy variables as described above.
H2: The response will be different if isolated by industry segment.

An OLS specification same as the on the whole sample, but only on the data
pertaining to the specific industry
H3: The effect will still be present even after controlling for self-selection bias.

The Heckman Two Stage Regression technique will be employed to correct for
the self-selection bias.
H4: The effect within industry group will be homoscedastic

Tested by using the quantile regression (QR) techniques
One stage OLS Model
EV = α0 + β1i Sizet + β2i Lev+ β3i C/S+ β4i E/S+ β5 - 85i D + εit
where:

EV = Enterprise Value of the firm

Size = Ln of Market Cap

Lev = Long-term debt/Total Assets

C/S = CAPEX/Sales

E/S = EBIT/Sales

D = One of 81 possible dummy variables. Created by assigning a dummy based on
one of three possible diversification choices and twenty-seven industries

εit = Error term
Two stage Heckman Model
1st Stage Logit Regression for Lambda:
λGD = α0 + β1i Sizet + β2i Lev + β3i C/S + β4i E/S + β5i C/S t-1+ β6i C/S t-2 + β7i E/S t-1 + β8i
E/S t-2 + β9i AT t-1 + β10i AT t-2 + β11i G + εit
λBD = α0 + β1i Sizet + β2i Lev + β3i C/S + β4i E/S + β5i C/S t-1+ β6i C/S t-2 + β7i E/S t-1 + β8i
E/S t-2 + β9i AT t-1 + β10i AT t-2 + β11i G + εit
λBTD = α0 + β1i Sizet + β2i Lev + β3i C/S+ β4i E/S+ β5i C/S t-1+ β6i C/S t-2 + β7i E/S t-1 + β8i
E/S t-2 + β9i AT t-1 + β10i AT t-2 + β11i G + εit
Where:

λGD = firms which are geographically diversified

λBD = firms which are industrially diversified

λBTH = firms which are both industrially and geographically diversified

Size = Ln of Market Cap

Lev = Long-term debt/Total Assets

C/S = CAPEX/Sales

E/S = EBIT/Sales

AT t-1,t-2 = The respective one and two year lags of total assets

C/S t-1,t-2 = The CAPEX/Sales ratio of one and two year lags

E/S t-1,t-2 = The EBIT/Sales ratio of one and two year lags
Second Stage OLS including the calculated Lambda:
EV = α0 + β1i Sizet + β2i Lev + β3i C/S + β4i E/S + β5i C/S t-1+ β6i C/S t-2 + β7i E/S t-1 + β8i
E/S t-2 + β9i AT t-1 + β10i AT t-2 + β11i G + β12i λGD + β13i λBD + β14i λBTH + β15Di - β95Di +
εit

Where all variables are the same as the earlier 1st stage specification and the
dummy variables correspond to each of the three possible diversification and
industry portfolios
4. Empirical Results:
I begin by exploring how closely the results replicate those of the other studies investigating
both global and industrial diversification. For the paper the diversification discount was
calculated for matching of four, three and two digit SIC codes using either the median or
geometric mean value as commented on above. For brevity only the two and four digit SIC with
geometric mean is shown, as all results were similar to one another. The use of the four digits
SIC with geometric mean was selected because of the already previously cited work.
(1)
lnvaldif
(2)
lnvaldif
(3)
VALDIF
(4)
VALDIF
GeoDum
-0.223***
(-7.70)
-0.175***
(-11.11)
-0.329***
(-7.08)
BusDum
-0.154***
(-11.60)
-0.146***
(-23.04)
-0.174***
(-8.16)
-0.123***
(-11.58)
Both
-0.382***
(-4.82)
-0.346***
(-9.58)
-0.784***
(-6.16)
-0.478***
(-7.83)
size
0.155***
(37.88)
0.160***
(84.18)
0.154***
(23.47)
0.184***
(57.62)
-254.9***
(-25.65)
-126.5***
(-44.36)
-298.5***
(-18.68)
-165.3***
(-34.37)
capexpersa~s
0.850***
(13.75)
0.185***
(17.30)
0.972***
(9.78)
0.142***
(7.88)
ebitpersales
0.149***
(4.99)
-0.0746***
(-6.18)
0.431***
(9.00)
-0.177***
(-8.67)
debtperMV
xrdpersales
0.630***
(12.84)
advpersales
0.239**
(1.98)
_cons
N
R-sq
-0.0472*
(-1.78)
1.096***
(13.89)
0.381*
(1.96)
-1.728***
(-36.02)
-1.757***
(-78.31)
-1.547***
(-20.05)
-1.917***
(-50.68)
8993
0.266
40622
0.213
8993
0.154
40622
0.116
t statistics in parentheses
* p<0.1, ** p<0.05, *** p<0.01
.
The first two columns report a diversification discount using the two-digit SIC geometric
mean specification while, the latter two shows the four digit specification. Column two, shows
results that are very similar to those presented by Denis et al. (2002). It can be observed that the
results are similar, different between the two and four digit specifications.
Currently, the only paper to have investigated the effects of industry on the diversification
discount was Santalo and Becerra. In their paper they reported that the effect of diversification is
not homogenous, as implied in other studies. However, for their work they did not look at
industry per se, but rather how diversification within industries affects diversification. Most
specifically they reported that diversification within industries that are heavily dominated by
diversified firms gives a premium for diversification. On the other hand they reported that being
diversified in an industry which is predominately controlled by single segment firms, results in a
diversification discount. Additionally, they do not attempt to explain why this result may be
observed.
Conversely, this paper follows the industry specifications in Fama and French (1997), and
constructs 27 industrial portfolios. These portfolios more accurately illustrate how the SIC code
classifications relate to each as opposed to using either a two or three SIC code methodology.
Additionally, even with some forty-thousand observations there are industries at the two digit
level that have few observations and would yield little power from any tests run using them as
the only data set.
Regressions were run using the full sample, using pooled OLS methods done to observe the
effects if any from being in a different industry with differing levels of diversification.
Additionally, regressions were also run within each of the samples, while direct comparison is
not possible in this way; it is possible to see how different industries respond to diversification.
Table 2 shows the regression results, perhaps more beneficially Figure 1(above) shows the
results in a graphical manner. In figure shows the coefficients of the various portfolio and
diversification dummy variables. It can be seen the response to diversification is not
homogenous, and can vary significantly. Two different specifications were run, the differences
being found in the control variables used. The first follows the specification of Denis et al. the
second follows a modified Campa and Kedia methodology. Here, Campa and Kedia is
introduced to have reference for later in the paper when controlling for possible endogeneity
bias. Refer to appendix A for the pooled OLS results by industry characteristics.
1
VALDIF
0.201***
-63.05
2
VALDIF
0.245***
-44.81
1
VALDIF
-0.107**
(-2.13)
2
VALDIF
-0.135**
(-2.19)
1
VALDIF
0.0896***
-2.77
2
VALDIF
0.161***
-4.06
1
VALDIF
0.636***
-7.56
2
VALDIF
0.496***
-5.54
Lev
-0.754***
(-27.95)
-0.788***
(-24.42)
hshldG
-0.00721
(-0.03)
-0.1
(-0.50)
cnstrG
0.0519
-0.32
0.133
-0.61
carryG
1.049***
-3.86
1.237***
-2.83
capexpersa~s
0.202***
-10.5
0.251***
-7.45
o.hshldBT
0
(.)
0
(.)
cnstrBT
0.357
-0.73
2.898***
-217.17
o.carryBT
0
(.)
0
(.)
ebitpersales
-0.206***
(-8.94)
-0.177***
(-4.77)
apparelB
-0.217***
(-5.09)
-0.208***
(-4.27)
steelB
-0.303***
(-8.30)
-0.253***
(-6.13)
minesB
0.0807
-1.01
0.0532
-0.56
foodB
0.483***
-11.85
0.525***
-11.14
apparelG
-0.333***
(-5.56)
0
(.)
steelG
-0.0927
(-0.80)
-0.0728
(-0.61)
minesG
0.312**
-2.55
0.143
-1.01
foodG
0.232*
-1.66
0.173
-1.27
o.apparelBT
0
(.)
0
(.)
steelBT
-0.672***
(-3.56)
-0.510*
(-1.96)
minesBT
-0.694**
(-2.03)
-0.427
(-1.10)
foodBT
-0.388
(-1.32)
-0.524
(-1.43)
hlthB
0.135***
-4.89
0.175***
-5.49
fabprB
0.235***
-8.75
0.252***
-7.88
coalB
-0.829***
(-5.26)
-0.789***
(-3.32)
booksB
-0.327***
(-9.80)
-0.278***
(-7.56)
hlthG
0.580***
-6.32
0.679***
-6.36
fabprG
0.222
-1.47
0.17
-0.97
coalG
-0.297***
(-2.95)
-0.208*
(-1.78)
booksG
-0.387
(-1.59)
-0.39
(-1.23)
hlthBT
0.38
-0.68
-0.565*
(-1.68)
fabprBT
0.471***
-38.54
0
(.)
coalBT
-1.354***
(-8.64)
-1.294***
(-7.20)
o.booksBT
0
(.)
0
(.)
chemsB
0.191***
-2.66
0.306***
-3.45
elceqB
0.258***
-6.25
0.341***
-7.24
oilB
-0.513***
(-19.68)
-0.479***
(-14.67)
gamesB
-0.305***
(-7.91)
-0.251***
(-5.38)
chemsG
0.977**
-2.42
1.821**
-2.28
elceqG
-0.0453
(-0.26)
-0.0949
(-0.48)
oilG
-0.731***
(-9.80)
-0.682***
(-6.92)
gamesG
-0.360***
(-4.44)
-0.286***
(-5.01)
chemsBT
-0.0534***
(-4.99)
0
(.)
o.elceqBT
0
(.)
0
(.)
oilBT
-0.808***
(-5.45)
gamesBT
-0.729***
(-3.52)
-0.579***
(-26.52)
-0.580**
(-2.40)
-0.629***
(-23.57)
txtlsB
0.194***
-4.25
0.256***
-4.6
autoB
0.144***
-3.22
0.144***
-2.86
-0.785***
(-4.81)
0.00000313
-0.64
txtlsG
-0.322*
(-1.85)
-0.217
(-1.05)
autoG
0.153
-1.05
0.250**
-2.02
-0.595***
(-10.21)
-0.649***
(-10.15)
o.txtlsBT
-0.349*
(-1.84)
-0.217
(-0.98)
mealsB
0
(.)
-0.0674
(-1.39)
autoBT
servsBT
0
(.)
-0.0955**
(-2.31)
-1.079***
(-138.56)
-1.221***
(-27.51)
lagcapexpe~s
0
(.)
-1.265*** lag2capexp~s
(-24.49)
0.00289
-0.13
buseqB
0.131***
-7.16
0.148***
-6.97
mealsG
0.204
-1.38
0.252**
-2.13
wholeG
-0.628***
(-3.59)
-0.505**
(-2.21)
lagebitper~s
0.0119
-0.36
buseqG
0.0802
-1.28
0.122*
-1.66
mealsBT
0.986***
-15.19
1.177***
-56.33
wholeBT
-2.697***
(-268.42)
0
(.)
lag2ebitpe~s
-0.0104
(-0.37)
buseqBT
-0.618
(-1.61)
0.132
-1.06
-1.133***
(-11.07)
0.124
-1.24
otherB
-0.460***
(-3.94)
-0.259
(-1.54)
rtailB
-0.225***
(-6.98)
-0.160***
(-4.31)
lnGDP
-0.401***
(-13.93)
otherG
-1.670***
(-6.19)
-1.918***
(-69.24)
rtailG
-0.439***
(-4.12)
-0.368**
(-2.56)
_cons
0.148
-0.75
0.395
-1.64
otherBT
1.238***
-2.94
-0.311***
(-6.66)
0.708**
-2.16
-0.291***
(-8.31)
rtailBT
0.776*
-1.65
-0.409***
(-10.30)
0.525**
-2.28
-0.265***
(-8.51)
-1.209***
(-148.62)
-0.0573*
(-1.88)
0
(.)
-0.0137
(-0.36)
paperG
-0.361***
(-4.47)
-0.279***
(-2.98)
telcmG
-0.522***
(-8.13)
-0.405***
(-5.92)
-0.438***
(-3.64)
-0.505***
(-3.16)
paperBT
-0.450*
(-1.85)
-0.121
(-0.44)
telcmBT
-0.608***
(-7.15)
-0.25
(-1.48)
-0.329***
(-3.77)
-0.338***
(-3.27)
size
hshldB
cnstrB
carryB
lagAT
servsB
servsG
viceB
viceG
viceBT
transB
transBT
transG
lag2AT
paperB
wholeB
telcmB
N
R-sq
-0.0000297***
(-4.18)
0.0176
-0.48
-2.057***
(-54.98)
40622
0.174
1.042***
-4.12
27088
0.214
From Table 2 it is possible to see that there is varying levels of significance and direction for
all three dummy variable types, depending upon the industry. Typically, the level of significance
as well as the sign is uniform regardless of the OLS model. On only six occasions was the
coefficient of the dummy variable significant in one model, yet insignificant under the other
condition. There are more negative coefficients than positive, the stands to reason as the overall
average effect is negative, even after corrections for some of the bias discussed in the literature
review. In the regressions of the three possible outcomes statistically insignificant occurs for the
dummy variables the least. Also, note that some of the dummy specifications are omitted from
the model because there are no observations within the data set.
From the results displayed and those listed in the appendix it can be observed that both the
first and second hypotheses hold. The response is not homogeneous under either the
specifications. The results imply that certain industries have characteristics that make certain
types of diversification more beneficial. In some cases this may be becoming an industrially
diversified firm, in others perhaps expanding globally and yet in others it seems to be both
feasible and prudent to expand both globally and industrially. Clearly, the governance and
additional costs of diversification are present, and special circumstances must exist to overcome
these additional costs in order to prevent value destruction within an industry class.
In this next section self-selection bias will be considered. While there are a number of
possible ways to control for possible self-selection bias including dynamic panel modeling,
fixed-effect, or random effect models, these require panel data to be used properly. While in a
sense the data being used for the study is an unbalanced panel set, the fact that firms “disappear”
and then return to the set as the years progress makes this technique unusable. As a result a
different approach needs to be used that is suitable for pooled data analysis.
The methodology discussed for Heckman’s Two Stage regression in Campa and Kieda
(2002) is used to test correct of endogeneity bias. The significant control variables used in their
specifications were used in addition to the log of real GDP. This variable was suggested and
used in Villonga (2004b). In the Heckman’s Two Stage model first a dichotomous variable is
selected and the chosen independent control variables are used in a logistic regression. This
specification referred to as either Lambda or the Inverse-Mills ratio is then introduced into the
second stage of the regression. This second stage is merely the original OLS regressions, but
with the Inverse Mill’s ratio as an independent variable in the second stage.
(1)
GeoDum
main
size
lagAT
lag2AT
0.291***
(13.99)
-0.0000169
(-1.42)
(2)
BusDum
0.194***
(22.23)
0.0000201*
(1.78)
(3)
Both
0.340***
(6.79)
0.0000140*
(1.70)
0.0000173
(1.33)
-0.0000431***
(-3.18)
-0.00000845
(-0.74)
capexpersa~s
0.101
(0.65)
-0.715***
(-6.23)
0.214
(0.66)
lagcapexpe~s
0.341**
(2.38)
-0.367***
(-3.10)
-0.113
(-0.33)
lag2capexp~s
0.175**
(2.05)
-0.327***
(-3.61)
0.214
(1.38)
-0.325***
(-3.92)
0.646
(1.15)
ebitpersales
-0.00276
(-0.02)
lagebitper~s
-0.207
(-1.53)
0.0203
(0.22)
0.597
(0.94)
lag2ebitpe~s
0.0263
(0.20)
0.107
(1.47)
0.630
(1.27)
Lev
-0.523***
(-3.11)
lnGDP
_cons
N
R-sq
1.422***
(8.44)
-19.99***
(-13.21)
27088
-0.601***
(-9.51)
0.808**
(2.50)
-0.0675
(-1.11)
4.572***
(7.68)
-1.509***
(-2.81)
27088
t statistics in parentheses
* p<0.1, ** p<0.05, *** p<0.01
-52.53***
(-9.60)
27088
If the Inverse Mill’s ratio
is negative it implies that the
firm was underperforming its
peers in the cross-section (as
a result of diversity). On the
other hand if the ratio is
positive than it implies that
the firms were outperforming
their peers prior to
diversification. In recent
literature even after
controlling for the
endogeneity bias a discount is
still observed. The first stage
of the regression is found in
Table 3. Since we have three
possible choices, to be solely
industrially diversified, solely
globally diversified or both
industrially and globally
diversified there are three logistic regressions.
It can be observed that the effects of the controls upon the dichotomous variable in question
are in general not uniform. The log of market capitalization (size) and leverage are the only
(1)
VALDIF
BusDum
-0.103***
(-8.05)
independent
variable which is significant for all three. Interestingly,
(2)
VALDIF
firms leverage flips from being negative and significant for industrial
-0.102***
(-7.99)
and global diversification, but for both leverage became positive.
GeoDum
-0.0562*
(-1.71)
(-1.62) (2009), investigates diversification from an international
Dastidar
-0.0528
Both
-0.429***
(-5.87)
-0.428***
setting
using this process, however, he does not investigate using all
(-5.81)
size
0.282***
(8.32)
0.281***
three
possible specifications, instead only calling a firm diversified or
(8.38)
not. As a result it is difficult to compare the results with those
lagAT
-0.0000335***
(-5.66)
-0.0000315***
(-5.34)
lag2AT
0.00000920
(0.87)
0.00000470
(0.45)
previously reported.
To the right in Table 4, the results for the second stage, OLS, portion
capexpersa~s
0.409***
(3.74)
is showed.
After correcting for the possible self-selection bias it can be
(3.46)
lagcapexpe~s
0.324***
(4.08)
seen0.274***
that the coefficients of the dummy variables in question are still
lag2capexp~s
0.137***
(2.91)
0.366***
(3.68)
negative.
0.106**While the effect for both the industrial diversification and
(2.41)
geographically diversified firms are reduced, with the global dummy
ebitpersales
-0.115*
(-1.77)
lagebitper~s
-0.150***
(-3.95)
-0.135***
(-3.64)
industrially and
lag2ebitpe~s
-0.0661*
(-1.91)
zero.
Of note, while all three Inverse Mill’s ratios (IMR) are significant
(-1.93)
Lev
-0.844***
(-7.64)
the-0.860***
coefficient for industrially diversified firms is only weakly
lnGDP
GeoPredict
BusPredict
BothPredict
_cons
N
R-sq
-0.230***
(-5.37)
-9.281***
(-10.42)
becoming weakly significant, when a firm is diversified both
globally, the change is not significantly different from
-0.0671*
(-7.96)
significant,
-0.360*** while the other two are highly significant.
(-47.84)
The signs of the coefficient are also of note. It can be observed that
-7.933***
(-11.39)
only the Lambda for geographic diversification is negative, while the
1.219*
(1.69)
(1.38)
other
two are positive. This would imply that firms that diversified
9.544***
(7.48)
globally
(7.25) are underperforming their peer group prior to the choice to
-1.299***
(-3.10)
27088
0.149
t statistics in parentheses
* p<0.1, ** p<0.05, *** p<0.01
.
-0.140**
(-2.23)
0.962
9.597***
diversify. However, the other two coefficients (industrial and both
27088
0.157
industrially and globally) are positive, implying better performance prior to being diversified in
those fashions. These findings are different than those reported in previous papers. In all
previous instances, even when a diversification discount was still present the IMR was negative.
While discussing the IMR another point is the magnitude of the coefficient. The order of
magnitude of the coefficients found for this study is much larger than any reported in prior
literature. The significance of this finding is not totally understood. This is because the outcome
(the effect on the dummy variables in question) is the same as that documented in the recent
literature. Namely, a reduction in the diversification discount is observed for all three groups,
but a discount is still present and significant for all three (even though the geographic dummy is
now weakly significant).
Next, will be discussed the effects of using Heckman’s Model in conjunction with all of the
dummy variables to control for industry as well as diversity. In Table 5 below, we have two
regression outputs; the first is the specification is the same as the second specification in Table 2.
Campa and Kedia’s controls and specifications, the second is the Two Stage Heckman’s
regression to control for heterogeneity (self-selection bias). The main results are as follows.
First, when comparing the control variables it is seen that both the first and second lags of
CAPEX/Sales become significant in the two stage specification. Additionally, the first lag of
EBIT/Sales also becomes significant the impact of real GDP is less negative, while leverage is
more negative, all of the other variables remain essentially unchanged.
Comparing the IMR from the specification with and without the industry dummies it can be
observed that for geographic and “both” the effects are essentially the same. The international
diversification dummy is significant and negative while the “both” dummy is significant and
positive. This implies that within industry those firms that select to diversify internationally, do
underperform their peers within their own industry, but firms that select to be fully diversified
(globally and industrially) typically are performing better than their peers. However, if the
business IMR coefficient is observed, it can be seen that it is now insignificant. This implies that
apparent underperformance in industrial diversification only is in fact more associated with
actual industry effects. If controlling for different industries helps to remove the significance,
this would imply that within industries businesses that choose to diversify perform prior to
diversification no worse, nor better than peers within their industry.
Upon examination of the coefficients for the regression output it can be seen that the
response to the addition of the correction is also not homogenous. This finding supports the third
hypothesis of the paper. For some industries the discount is lessened or the premium is increased
while in others the discount is increased or the premium is decreased. There is one instance each
where a coefficient moved from being significant to insignificant (hltB) and vice versa
(telecmBT). While the changes in coefficients are not statistically different from one another in
the two models there are a number of cases where a notable change in discount or premium can
be observed. For example the following all exhibit more premium from diversification hlthG,
autoG, viceBT and ChemB as well as MealsBT show less premium. The discount side sees more
changes, all of the following dummies have less discount after the correction: OilB, OilG, OilBT,
telcmG and retailB. Finally, looking at dummies that show more discount after correction the
following coefficients qualify: booksB, gamesG, CoalG, paperB, paperG, transG, transBT and
mealsB. From these results it is clear that the reaction to diversification even after correcting for
self-selection bias is not homogenous across different industry specifications.
N
R-sq
otherBT
otherG
otherB
_cons
BothPredict
BusPredict
GeoPredict
lnGDP
Lev
lag2ebitpe~s
lagebitper~s
ebitpersales
lag2capexp~s
lagcapexpe~s
capexpersa~s
lag2AT
lagAT
size
27088
0.214
27088
0.218
transBT
transG
1
2
OLS
Heckman
0.245***
0.297***
foodB
-44.81
-9.06
0.00000313 -0.0000280*** foodG
-0.64
(-4.93)
-0.0000297*** 0.00000445
foodBT
(-4.18)
-0.44
0.251***
0.363***
booksB
-7.45
-3.46
0.0176
0.291***
booksG
-0.48
-3.81
0.00289
0.112**
o.booksBT
-0.13
-2.49
-0.177***
-0.147**
gamesB
(-4.77)
(-2.33)
0.0119
-0.132***
gamesG
-0.36
(-3.62)
-0.0104
-0.0467
gamesBT
(-0.37)
(-1.41)
-0.788***
-0.904***
hshldB
(-24.42)
(-8.42)
-0.401***
-0.137***
hshldG
(-13.93)
(-3.25)
-8.869***
o.hshldBT
(-10.31)
0.642
steelB
-0.92
8.234***
steelG
-6.53
1.042***
-2.056***
steelBT
-4.12
(-4.98)
-0.259
-0.349**
viceB
(-1.54)
(-2.36)
-1.918***
-1.946***
viceG
(-69.24)
(-59.96)
0.708**
0.704**
viceBT
-2.16
-2.14
transB
1
OLS
0.525***
-11.14
0.173
-1.27
-0.524
(-1.43)
-0.278***
(-7.56)
-0.39
(-1.23)
0
(.)
-0.251***
(-5.38)
-0.286***
(-5.01)
-0.580**
(-2.40)
-0.135**
(-2.19)
-0.1
(-0.50)
0
(.)
-0.253***
(-6.13)
-0.0728
(-0.61)
-0.510*
(-1.96)
0.124
-1.24
0.395
-1.64
1.238***
-2.94
-0.311***
(-6.66)
-0.338***
(-3.27)
-0.505***
(-3.16)
2
1
Heckman
OLS
0.534***
apparelB -0.208***
-11.35
(-4.27)
0.211
o.apparelG
0
-1.52
(.)
-0.508 o.apparelBT
0
(-1.42)
(.)
-0.291***
hlthB
0.175***
(-7.88)
-5.49
-0.377
hlthG
0.679***
(-1.22)
-6.36
0
hlthBT
-0.565*
(.)
(-1.68)
-0.256***
chemsB
0.306***
(-5.48)
-3.45
-0.301***
chemsG
1.821**
(-5.32)
-2.28
-0.586** o.chemsBT
0
(-2.50)
(.)
-0.139**
txtlsB
0.256***
(-2.28)
-4.6
-0.133
txtlsG
-0.217
(-0.67)
(-1.05)
0
o.txtlsBT
0
(.)
(.)
-0.249***
cnstrB
0.161***
(-6.09)
-4.06
-0.0951
cnstrG
0.133
(-0.78)
-0.61
-0.543**
cnstrBT
2.898***
(-2.11)
-217.17
0.109
mealsB
-0.0674
-1.09
(-1.39)
0.303
mealsG
0.252**
-1.15
-2.13
1.305***
mealsBT
1.177***
-3.09
-56.33
-0.310***
wholeB
-1.265***
(-6.63)
(-24.49)
-0.422***
wholeG
-0.505**
(-4.04)
(-2.21)
-0.628*** o.wholeBT
0
(-3.84)
(.)
2
Heckman
-0.210***
(-4.31)
0
(.)
0
(.)
0.174***
-5.47
0.693***
-6.41
-0.521
(-1.47)
0.285***
-3.2
1.816**
-2.26
0
(.)
0.251***
-4.53
-0.249
(-1.20)
0
(.)
0.166***
-4.19
0.127
-0.58
2.876***
-213.1
-0.0799*
(-1.66)
0.251**
-2.11
1.132***
-52.81
-1.247***
(-24.14)
-0.528**
(-2.34)
0
(.)
o.rtailBT
rtailG
rtailB
minesBT
minesG
minesB
o.carryBT
carryG
carryB
o.autoBT
autoG
autoB
o.elceqBT
elceqG
elceqB
o.fabprBT
fabprG
fabprB
1
OLS
0.252***
-7.88
0.17
-0.97
0
(.)
0.341***
-7.24
-0.0949
(-0.48)
0
(.)
0.144***
-2.86
0.250**
-2.02
0
(.)
0.496***
-5.54
1.237***
-2.83
0
(.)
0.0532
-0.56
0.143
-1.01
-0.427
(-1.10)
-0.160***
(-4.31)
-0.368**
(-2.56)
0
(.)
2
Heckman
0.252***
-7.84
0.181
-1.03
0
(.)
0.336***
-7.18
-0.0988
(-0.51)
0
(.)
0.145***
-2.84
0.299**
-2.25
0
(.)
0.490***
-5.55
1.244***
-2.91
0
(.)
0.0862
-0.93
0.185
-1.34
-0.331
(-0.86)
-0.123***
(-3.37)
-0.374**
(-2.55)
0
(.)
paperBT
paperG
paperB
buseqBT
buseqG
buseqB
servsBT
servsG
servsB
telcmBT
telcmG
telcmB
oilBT
oilG
oilB
coalBT
coalG
coalB
1
OLS
-0.789***
(-3.32)
-0.208*
(-1.78)
-1.294***
(-7.20)
-0.479***
(-14.67)
-0.682***
(-6.92)
-0.785***
(-4.81)
-0.0137
(-0.36)
-0.405***
(-5.92)
-0.25
(-1.48)
-0.629***
(-23.57)
-0.649***
(-10.15)
-0.217
(-0.98)
0.148***
-6.97
0.122*
-1.66
-1.133***
(-11.07)
-0.291***
(-8.31)
-0.279***
(-2.98)
-0.121
(-0.44)
2
Heckman
-0.797***
(-3.40)
-0.251**
(-2.22)
-1.296***
(-7.16)
-0.466***
(-14.85)
-0.649***
(-6.95)
-0.615***
(-3.99)
-0.0316
(-0.83)
-0.389***
(-5.75)
-0.332**
(-2.45)
-0.637***
(-23.95)
-0.644***
(-9.85)
-0.251
(-1.12)
0.142***
-6.71
0.132*
-1.79
-1.125***
(-10.62)
-0.301***
(-8.63)
-0.315***
(-3.38)
-0.153
(-0.55)
In the final section we will look at using quantile regression techniques to analyze the
differing effects of diversification. Lee and Li (2012), develop methodologies that can be
employed to investigate diversification discount using this type of approach. They comment
upon the fact that if a sample exhibits a somewhat high degree of heterogeneity at departures
from the median standard OLS regressions will yield poor descriptive results. The methodology
employed for their paper uses a diversification measure established as one minus a computed
value for the Herfindahl index. They report that at high levels of profitability there is a
significant and negative relationship to diversification. In the same paper the authors adjust for
risk and report that the discount is removed.
Using similar methodology but with the enterprise value differential that has been used
throughout the paper I intend to investigate the issue further using industry specifications.
Because of the length associated with the output for this investigation the results are placed in
Appendix B. First we follow the techniques outlined in Lee and Li using the same control
variables as well as dummy variables to control for the year. Instead of using the few dummies
they report for one digit SIC code for industry the 27 industry portfolio used for the rest of the
paper was used in this regression. Comparing the results obtained for this paper compared to
those reported by Lee and Li it is found for instance that the relationship with size becomes
negative in the highly profitable companies, I however to not find this to be the case. Instead size
is always positively significant (while the coefficient does decrease in size). The only change in
specifications between their paper and the results shown here is how the industry portfolios were
constructed; the results can be found in the appendix.
Using the 81 dummy variables to control for industry and diversification, a test to see the
connection between excess enterprise value and profitability was conducted. It is possible to
refer to Table Six to see these results. In order to create a manageable table only the controls and
the enterprise value are shown, the dummy variables are not shown. As a note Lee and Li use
ROE as their measure of profitability, where I use ROA. This is a slight difference but, should
not impact results in a significant manner. Where Lee and Li report that diversity is positively
associated with poor performing firms (ROE 15% or less) and negatively associated with better
performing firms (ROE 75% or greater) it is found that after controlling for diversification and
industry affects enterprise value differentials are positively associated with better performing
firms (ROE >60%) and insignificant for the rest of the sample. Also, they reported that at high
ROE >80% size became negative. They interpreted this as a sign that becoming too large when a
profitable company is detrimental. Under these conditions perhaps a company should scale
back.
ROA
Coef.
Bootstrap
Std. Err.
t
P>t
q10
VALDIF
0.0019725
size
0.0060411
capexpersales-0.0919788
ebitpersales 0.7508427
Lev
-0.1379977
lnGDP
-0.1204442
0.0014129
0.0006963
0.0060567
0.0163724
0.0074355
0.0059844
1.4
8.68
-15.19
45.86
-18.56
-20.13
0.163
0
0
0
0
0
q60
VALDIF
0.0012983
size
0.0033732
capexpersales-0.0300183
ebitpersales 0.4326144
Lev
-0.0985015
lnGDP
-0.0297184
0.0003352
0.0001742
0.0037453
0.0076071
0.0016944
0.0010848
3.87
19.37
-8.01
56.87
-58.13
-27.4
0
0
0
0
0
0
q20
VALDIF
0.0008954
size
0.0041297
capexpersales-0.0748153
ebitpersales 0.6175498
Lev
-0.1207442
lnGDP
-0.0725819
0.0006755
0.00038
0.0052773
0.0093851
0.0032978
0.0032947
1.33
10.87
-14.18
65.8
-36.61
-22.03
0.185
0
0
0
0
0
q70
VALDIF
0.0025708
size
0.0036178
capexpersales-0.0216804
ebitpersales 0.3910614
Lev
-0.1028765
lnGDP
-0.0234157
0.0004069
0.0001844
0.0027637
0.0080908
0.0018961
0.0012953
6.32
19.62
-7.84
48.33
-54.26
-18.08
0
0
0
0
0
0
q30
VALDIF
0.0001285
size
0.0033777
capexpersales-0.0608785
ebitpersales 0.5518125
Lev
-0.1077212
lnGDP
-0.0500017
0.0003572
0.000237
0.0042198
0.0066423
0.0023764
0.0018888
0.36
14.25
-14.43
83.08
-45.33
-26.47
0.719
0
0
0
0
0
q80
VALDIF
0.0046892
size
0.0038361
capexpersales-0.0142872
ebitpersales 0.3364892
Lev
-0.1058051
lnGDP
-0.0150432
0.000427
0.0002026
0.0028672
0.0102192
0.0024013
0.0016819
10.98
18.93
-4.98
32.93
-44.06
-8.94
0
0
0
0
0
0
q40
VALDIF
0.0004225
size
0.003174
capexpersales-0.0500842
ebitpersales 0.5065098
Lev
-0.101461
lnGDP
-0.0395558
0.0002749
0.000211
0.002501
0.007674
0.0017758
0.0011232
1.54
15.05
-20.03
66
-57.14
-35.22
0.124
0
0
0
0
0
q90
VALDIF
0.0083116
size
0.0047597
capexpersales-0.0091948
ebitpersales 0.2650564
Lev
-0.1073224
lnGDP
0.0024163
0.0007339
0.0005514
0.0051418
0.0111146
0.0038919
0.0029456
11.32
8.63
-1.79
23.85
-27.58
0.82
0
0
0.074
0
0
0.412
q50
VALDIF
0.0007576
size
0.0033061
capexpersales-0.0398188
ebitpersales 0.4707907
Lev
-0.0985157
lnGDP
-0.0348601
0.000299
0.00015
0.0031519
0.0064904
0.0014209
0.0009142
2.53
22.04
-12.63
72.54
-69.33
-38.13
0.011
0
0
0
0
0
Next, the same regression approach was used to test for the relationship between the
uniformity of the regression results between the 81 dummies testing for the diversification effects
and enterprise value. This is done to see if the discount or premium noted for each type of
diversification for a given industry is uniform across the quantiles of enterprise value in the
cross-section. To the surprise of the author, the response for some industry and diversification
was not uniform. This goes against the last hypothesis. While all of the results are not listed
within the paper it was observed that for some industries the impact on enterprise value was
uniform after considering individually the three ways in which one could diversify. However, for
other industry specifications the response was not uniform. This stands contradictory to the final
hypothesis of the paper. Clearly, additional investigation into this topic is warrented.
5. Conclusion:
After testing for the presence of homogeneity in diversification effect using a number of
different econometric techniques. It can be solidly said that diversification does affect a firm’s
enterprise value. However, the result is not uniform across industries as prior literature either
assumes or directly implies. A total possible number of 81 dummy variables were used to test for
the effect, in a pooled OLS format. After eight dummy variables were removed because of their
lack in the sample 73 remained. A total of 35 dummies were significant and negative with
coefficient values ranging between -.11525 and -2.000322, seventeen variables were found to be
statistically insignificant and 22 coefficients were found to be positive with values ranging from
.054291 and 1.087618.
Since, a large portion of the literature investigates the relation of how the effect can change
as a result of endogeneity (self-selection bias). After, using commonly accepted methodologies
to control for this bias it is found that the response to diversification is not uniform and does not
go away after applying the Heckman Two stage regression techniques. Additionally, the
response to applying the Heckman technique is not uniform.
Lastly, the use of quantile regression techniques was used to test for the uniformity of the
diversification effect for each of the industries across both enterprise value and operational
efficiency (ROA). Even after exploring the response within industry and diversification
specification it can be seen that for some industries the response is still not uniform. \
References
Aggarwal, R. K., & Samwick, A. A. (2003). Why Do Managers Diversify Their Firms? Agency
Reconsidered. The Journal of Finance, 71-118.
Ahn, S., Denis, D. J., & Denis, D. K. (2006). Leverage and investment in diversified firms.
Journal of Financial Economics, 317-337.
Amihud, Y., & Lev, B. (1981). Risk reduction as a managerial motive for conglomerate merger.
Bell Journal of Economics, 605-617.
Ammann, M., Hoechle, D., & Schmid, M. (2012). Is there Really No Conglomerate Discount?
Journal of Business Finance & Accounting, 264-288.
Aybar, B., & Ficici, A. (2009). Cross-border acquisitions and firm value: An analysis of
emerging-market multinationals. Journal of International Business Studies, 1317-1338.
Bartelsman, E. J., & Beetsma, R. M. (2003). Why pay more? Corporate tax avoidance through
transfer pricing in OECD countries. Journal of Public Economics, 2225–2252.
Benito-Osorio, D., Guerras-Martin, L., & Zuniga-Vicente, J. (2012). Four decades of research on
product diversification: A literature review. Management Decision, 325-344.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial
Economics, 39-65.
Borghesi, R., Houston, J., & Naranjo, A. (2007). Value, Survival, and the Evolutoin of Firm
Organizational Structure. Financial Management, 5-31.
Buckley, P. J. (1988). The limits of explanation: Testing the internalization theory. Journal of
International Business Studies, 181-194.
Campa, J. M., & Kedia, S. (2002). Explaining the Diversification Discount. The Journal of
Finance, 1731-1762.
Caves, R. E. (1971). International corporatoins: The industrial economics of foreign investment.
Economica, 1-27.
Caves, R. E. (1996). Multinational enterprise and economic analysis (2nd ed.). Cambridge, MA:
Harvard University Press.
Creal, D. D., Robinson, L. A., Rogers, J. L., & Zechman, S. L. (2012). The Multinational
Advantage. Boothe School Working Paper.
Dastidar, P. (2009). International corporate diversification and performance: Does firm selfselection matter? Journal of International Business Studies, 71-85.
Denis, D. J., Denis, D. K., & Sarin, A. (1997). Agency Problems, equity ownership, and
corporate diversificatoin. The Journal of Finance, 135-160.
Denis, D. J., Denis, D. K., & Yost, K. (2002). Global Diversification, Industrial Diversification,
and Firm Value. The Journal of Finance, 1951-1979.
Desai, M. A., Foley, C. F., & Hines Jr., J. R. (2004). A Multinational Perspective on Capital
Structure Choice and Internal Capital Markets. The Journal of Finance, 2451-2487.
Desai, M. A., Foley, C. F., & Hines Jr., J. R. (2006). Capital Controls, Liberalizations, and
Foreign Direct Investment. Review of Financial Studies, 1433-1464.
Desai, M. A., Foley, C. F., & Hines Jr., J. R. (2008). Capital Structure with risky foreign
investment. Journal of Financial Economics, 534-553.
Dos Santos, M., Errunza , V., & Miller, D. P. (2008). Does corporate international diversification
destroy value? Evidence from cross-border mergers and acquisitions. Journal of Banking
& Finance, 2716-2724.
Doukas, J. A., & Kan, O. B. (2006). Does Global Diversification Destroy Firm Value? Journal of
International Business Studies, 352-371.
Duchin, R. (2010). Cash Holdings and Corporate Diversification. The Journal of Finance, 955992.
Duchin, R., & Sosyura, D. (2012 ). Divisional Managers and Internal Capital Markets. The
Journal of Finance, Forthcoming.
Eckert, S., & Trautnitz, G. (Winter 2010/211). A Commentary on Risk Reduction by Geographic
Diversification. Multinational Business Review, 95-107.
Erdorf, S., Hartmann-Wendels, T., Heinrichs, N., & Matz, M. (2012). Corporate Diversification
and Firm Value: A Survey of Recent Literature. Cologne Graduate School Working
Paper.
Errunza , V., & Senbet, L. (1984). International corporate diversification, market valuation, and
size-adjusted evidence. The Journal of Finance, 727-743.
Fauver, L., Houston, J., & Naranjo, A. (2003). Capital Market Development, International
Integration, Legal Systems, and the Value of Corporate Diversification: A Cross-Country
Analysis. Journal of Financial and Quantitiative Analysis, 135-157.
Ferris, S. P., Sen, N., & Thu, N. (2010). Firm value and the diversification choice: international
evidence from global and industrial diversification. Applied Economics Letters, 10271031.
Gande, A., Schenzler, C., & Senbet, L. W. (2009). Valuation effects of global diversification.
Journal of International Business Studies, 1515-1532.
Gomes, J., & Livdan, D. (2004). Optimal Diversification: Reconciling Theory and Evidence. The
Journal of Finance, 507-535.
Graham, J. R., Lemmon, M. L., & Wolf, J. G. (2002). Does Corporate Diversification Destroy
Value? The Journal of Finance, 695-720.
Harris , M., Kriebel, H., & Raviv, A. (1982). Asymmetric Information, Incentives and Intrafirm
Resource Allocation. Management Science, 604-620.
Hoechle, D., Schmid, M., Walter, I., & Yermack, D. (2012). How much of the diversificatoin
discount can be explained by poor corporate governance? Journal of Financial
Economics, 41-60.
Jensen, M. C. (1986). Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers. The
American Economic Review, 323-329.
Jensen, M., & Murphy, K. J. (1990). Performance Pay and Top-Management Incentives. Journal
of Political Economy , 225-264.
Jones, G. R., & Hill, C. W. (1988). Transaction cost analysis of strategy-structure choice.
Strategic Management Journal, 159-172.
Kim, Y., & Mathur, I. (2008). The impact of geographic diversificatoin on firm performance.
International Review of Financial Analysis, 747-766.
Kogut, B., & Chang, S. (1991). Technological capabilities and Japanese foreign direct
investment in the United States. Rewview of Economics and Statistics, 401-413.
Kumar, M. (2009). The Relationship Between Product and International Diversification: The
Effects of Short-Run Constraints and Endogeneity. Strategic Management Journal, 99116.
Kuppuswamy, V., & Villalonga, B. (2010). Does Diversificatoin Create Value in the Presence of
External Financing Constraints? Evidence from the 2007-2009 Financial Crisis. Harvard
University Working Paper.
Laeven, L., & Levine, R. (2007). Is there a diversification discount in financial conglomerates?
Journal of Financial Economics, 331-367.
Lamont, O. A., & Polk, C. (2002). Does diversification destroy value? Evidence from the
industry schocks. Journal of Financial Economics, 51-77.
Lang, L., & Stulz, R. (1994). Tobin's Q, Corporate Diversification, and Firm Performance.
Journal of Political Economy, 1248-1280.
LaPorta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. (1997). Legal Determinants of
External Finance. The Journal of Finance, 1131-1150.
Lee, B., Li, M.-Y. L., & . (2012). Diversification and risk-adjusted performance: A quantile
regression approach. Journal of Banking & Finance, 2157-2173.
Lewellen, W. G. (1971). A Pure Financial Rationale for the Conglomerate Merger. The Journal
of Finance, 521-537.
Li, L. (2007). Multinationality and performance: A synthetic review and research agenda.
International Journal of Management Reviews, 117-139.
Lins, K., & Servaes, H. (1999). International Evidence on the Value of Corporate Diversification.
The Journal of Finance, 2215-2239.
Lyandres, E. (2007). Strategic Cost of Diversification. The Review of Financial Studies, 19011940.
Mansi, S. A., & Reeb, D. M. (2002). Corporate Diversification: What Gets Discounted? The
Journal of Finance , 2167-2183.
Martin, J. D., & Sayrak, A. (2003). Corporate diversification and shareholder value: a survey of
recent literature. Journal of Corporate Finance, 37-57.
Merton, R. C. (1974). On The Pricing of Corporate Debt: The Risk Structure Of Interest Rates.
The Journal of Finance, 449-470.
Moeller, S. B., & Schlingemann, F. P. (2005). Global diversification and bidder: A comparison
between cross-border and domestic acquisitions. Journal of Banking & Finance, 533564.
Ozbas, O., & Scharfstein, D. S. (2010). Evidence on the Dark Side of Internal Capital Markets.
Review of Financial Studies, 581-599.
Rajan, R., Servaes, H., & Zingales, L. (2000). The Cost of Diversity: The Diversification
Discount and Inefficient Investment. The Journal of Finance, 35-80.
Roth, K., & O'Donnell, S. (1996). Foreign Subsidiary Compensation Strategy: An Agency
Theory Perspective. Academy of Management Journal, 678-703.
Rudolph, C., Schwetzler, B., & . (2012). Conglomerate Discounts and Their Biases: An
International Survey. Working Paper.
Santalo, J., & Becerra, M. (2008). Competition from Specialized Firms and the DiversificationPerformance Linkage. The Journal of Finance, 851-883.
Scharfstein, D. S., & Stein , J. C. (2000). The Dark Side of Internal Capital Markets: Divisional
Rent-Seeking and Inefficient Investment. The Journal of Finance, 2537-2564.
Servaes, H. (1996). The Value of Diversification During the Conglomerate Merger Wave. The
Journal of Finance, 1201-1225.
Shleifer, A., & Vishny, R. W. (1989). Management entrenchment: The case of manager-specific
investments. Journal of Financial Economics, 123-139.
Stein, J. C. (1997). Internal capital markets and the competition for corporate resources. The
Journal of Finance, 111-133.
Teece, D. J. (1980). Economies of scope and the scope of the enterprise. Journal of Economic
Behavior & Organization, 223-247.
Villalonga, B. (2004). Diversification Discount or Premium? New Evidence from the Business
Informatoin Tracking Series. The Journal of Finance, 479-506.
Villalonga, B. (2004). Does Diversification Cause the "Diversificatoin Discount". Financial
Management, 5-27.
Volkov, N. (2012). Does corporate diversifcation enhance firm value during times of crisis?
Working Paper.
Whited, T. (2001). Is It Inefficient Investmet that Causes the Diversificatoin Discount? The
Journal of Finance, 1667-1691.
Yan, A., Yang, Z., & Jiao, J. (2010). Conglomerate investment under various capital market
conditions. Journal of Banking & Finance, 103-115.