Villalonga (2004)
• Lang and Stulz (1994), Berger and Ofek (1995), and Servaes (1996)
find that diversified firms trade at an average discount relative to
single-segment firms; this suggests diversification destroys value
• Villalonga (1999) and Campa and Kedia (2002) show that the
discount is only the product of sample selection biases; diversified
firms trade at a discount prior to diversifying, and when selection
bias is corrected for the diversification discount disappears
• This paper questions the “discount” further by investigating the
possibility that the discount is an artifact of Compustat’s segment
data
• Compustat provides disaggregated financial information for
business segments that represent at least 10% of a firm’s sales,
assets, or profits, and prior studies have mostly relied on this data to
determine which firms are diversified and how much
• The author uses a new census database instead
Problems with Compustat
1.
2.
3.
4.
5.
The extent of disaggregation in financial reporting is much lower
than the true extent of a firm’s industrial diversification; firms that
appear to be “single-segment” may not be
4 digit SIC Codes are too broad in many cases to pin down
exactly what activities the firm is undertaking
Financial accounting standards allow managers considerable
flexibility in how segments are constructed; the aggregation of
activities into any given segment differs from firm to firm
Segments are self-reported and Compustat assigns them into SIC
codes
Firms may change the segments they report even when there is
no real change in their operations; what appears to be
diversification or focusing may just be reporting changes
The Longitudinal Research Database (LRD)
• Some previous studies use the LRD
• The LRD covers only U.S. manufacturing establishments (plants)
• The new census database includes non-manufacturing
establishments
• This is important; less than 20% of all multi-segment firms in
Compustat’s segment files are manufacturing-only; 56% are nonmanufacturing only; only 24% are diversified across both sectors
• The majority diversified across both sectors have most of their
assets in non-manufacturing
The Business Information Tracking Series
(BITS)
• BITS is a new census database that covers the whole U.S. economy
at the establishment level
• These data allow the author to construct business units
• The author uses a common sample of firms and Lang and Stulz’s
method to compare the value estimates obtained using BITS to
those obtained using Compustat
• The author finds a diversification discount using Compustat
segments, but a diversification premium using BITS business units
• The results call into question the adequacy of segment data for
research in corporate finance, strategy, etc.
Explanations
The author offers two explanations for the results:
1.
Relatedness: Compustat segments measure mainly unrelated
diversification (because similar activities get grouped into one
segment), whereas BITS allows the author to measure all types of
diversification. Thus, the results suggest that related
diversification is associated with a premium and unrelated
diversification is associated with a discount
2.
Strategic Accounting: Diversified firms aggregate their activities
into segments in ways that make them appear to be poor
performers
Data
• BITS provides establishment level panel data between 1989 and
1996 for all U.S. private-sector establishments with positive payroll
in any of these years, from both private and public firms
• BITS includes over 50 million establishment-year observations from
over 40 million firm-years
• The basic unit of analysis in BITS is the business establishment,
defined as “a single physical location where business is conducted
or where services or industrial operations are performed”
• For each establishment-year observation, BITS contains information
on its employment, annual payroll, primary four-digit SIC code,
location, start year, the firm and legal entity to which the
establishment belongs, and the firm’s total employment
• A “legal entity” is a corporation, partnership, or sole proprietorship
• A “firm” in BITS is “the largest aggregation of business legal entities
under common ownership and control”
Coverage
• BITS contains data on the entire population of U.S. establishments
from all sectors of the economy, excluding farms, railroads, the
Postal Service, private households, and large pension, health, and
welfare funds
• BITS contains no performance data; the author links BITS with
Compustat to overcome this problem
• This limits the sample to publicly traded firms
• The author limits the sample further by following the previous
literature and excluding firms that operate in the financial sector,
agriculture, the government, etc.
• The author also eliminates observations with missing variables and
outliers (firms whose imputed q is higher than four times or lower
than a fourth of their actual q)
Sample
• The resulting sample has 1,555,371 establishment-year
observations from 12,708 different firm-years
• The author aggregates all of a firm’s establishments with a common
SIC code into “business units”
• This is the BITS-based analytical equivalent of the Compustat
segments, but the number of business units is not limited to 10
• The maximum number of business units within a firm is 133
• On average, there are 122 establishments per firm, 15.5
establishments per business unit, and 7.9 business units per firm
• The average number of segments per firm is 1.7 (4.6 business units
per segment)
• 43% of the sample switches from undiversified to diversified when
one moves from a segment analysis to a business unit analysis
• 21 percent of the firms have more than 10 business units
Limitations of BITS
• BITS includes only U.S. establishments and their employees
because it is U.S. Census data
• Compustat includes foreign operations, employees, etc.
• The author uses Compustat’s geographic segment files to determine
the percentage of U.S. vs. non-U.S. operations for each firm
• Tests suggest that the extent of coverage in BITS is uncorrelated
with the variables the author uses in this study, so the author uses
the full sample for most of the results
• The author checks the robustness of the main results by running
everything after excluding firms with non-U.S. operations
• Another limitation is that BITS still relies on 4 digit SIC codes, which
aggregate some unrelated activities and also distinguish between
some related activities (related in terms of production technology or
markets or both); SIC codes need not correspond to a firm’s actual
business units
Excess Value Measures
Excess Value is measured two ways:
1.
The difference between a firm’s Tobin’s q and its imputed q
2.
The natural logarithm of the ratio of Tobin’s q to its imputed q
•
Tobin’s q is measured using the ratio of the market value of
common equity plus the book value of preferred stock and debt to
total assets; Imputed q’s are measured two ways:
1.
In segment data, the imputed q is the asset weighted average of
the hypothetical q’s of the firm’s segments, where a segment’s
hypothetical q is the average of the single-segment firms in the
industry-year
2.
In BITS data, the imputed q is the employment weighted average
(assets are not measured by BITS) of the hypothetical q’s of the
firm’s business units, where the hypothetical q is the average of
the single-business unit firms
Industry q Measures
• How closely do the SIC codes in Compustat match those in BITS?
• The author checks this using the subsample of firms that are
classified as single-segment in Compustat
• 51% of the single-segment, single business unit firms have the
same 4 digit SIC code in both BITS and Compustat
• 9% match at the 3 digit level; 8% at the 2 digit level; 8% at the 1 digit
• The remaining firms (about 25%) do not match at all
• Among single segment but multibusiness firms, 84% choose their
single segment’s SIC from among the set of 4 digit codes they
operate in according to BITS
• However, only 14% choose to report the 4 digit SIC code of their
largest business unit (measured using employment, not assets)
• Overall, in 81% of the cases, firms choose a segment SIC code that
does not match that of its largest business
Results
•
•
•
The author compares the average excess value of multi-segment firms to
that of single segment firms and confirms the finding in the previous
literature: multi-segment firms trade at a statistically significant discount
relative to single-segment firms
When the author performs a similar analysis comparing multi-business unit
firms to single business unit firms, there is a statistically significant premium
The author performs several robustness checks, including using alternative
measures of industry q and more complex measures of diversification,
checking whether weighting segment q’s by employment instead of assets
is sufficient to remove the discount, restricting the sample to firms with
100% of their operations in the U.S., reconstructing business units while
imposing a 10% materiality condition similar to Compustat’s (using
employment instead of assets), and combining vertically related activities
into one business unit; the results hold up