The determinants and consequences of private equity buyouts of private firms: Evidence

from U.S. corporate tax returns

Jonathan B. Cohn*

University of Texas at Austin

Erin M. Towery

University of Georgia

December 2013

______________________________________________________________________________

ABSTRACT

This paper uses corporate tax return data to study the determinants and consequences of private

equity (PE) buyouts of U.S. private firms between 1995 and 2009. In contrast with prior

evidence that PE acquirers target public firms facing overinvestment problems, we find that PE

acquirers target private firms facing underinvestment problems due to financing constraints. We

then provide evidence that PE buyouts create value through their real effects on private firms in

two ways: by leading to operational turnarounds in struggling firms and by relaxing financing

constraints that limit the exercise of growth options in healthier firms.

JEL Classification codes: G34, G32, H25

Keywords: Private Equity Buyouts, Private Firms, Financing Constraints, Capital Structure

The Internal Revenue Service (IRS) provided confidential tax information to Towery pursuant to provisions of the

Internal Revenue Code that allow disclosure of information to a contractor to the extent necessary to perform a

research contract for the IRS. None of the confidential tax information received from the IRS is disclosed in this

treatise. Statistical aggregates were used so that a specific taxpayer cannot be identified from information supplied

by the IRS. We appreciate comments and suggestions received from Andres Almazan, Aydoğan Altı, Nick Bloom,

Jay Hartzell, Edie Hotchkiss, Mark Jansen, Steve Kaplan, Bob Parrino, Uday Rajan, and Sheridan Titman, as well as

seminar participants at the University of Texas at Austin. We gratefully acknowledge research support from the

McCombs Research Excellence Fund. John Marymont, Jordan Nickerson, Mitch Towner, and Zachary Zapinsky

provided excellent research assistance.

* Corresponding author. Physical address: McCombs School of Business, University of Texas at Austin, One

University Station B6400, Austin, TX 78712, USA. Telephone number: 512-232-6827. Facsimile number: 512-

471-5073. Email address: jonathan.cohn@mccombs.utexas.edu.

1

Private equity (PE) firms play a key role in the market for corporate control. Although

PE buyouts of publicly-traded firms receive more attention in the press, buyouts of private firms

are far more common (Davis et al. (2013)). However, little is known about the motivations for

and consequences of these buyouts as private firms, at least in the U.S., are generally not

required to disclose financial information. We fill this void by analyzing PE buyouts of private

firms using confidential data from U.S. corporate tax returns, which all U.S. corporations, public

or private, must file. We use these data to study the determinants of private firm buyouts, as well

as how operating performance, sales growth, and financial structure change around these

transactions. In doing so, we shed light on the motives for these buyouts and the means through

which PE firms create value.

Studying private firm buyouts is important not only because of the size and growth of this

market, but also because the factors driving these transactions likely differ from the factors

driving public firm buyouts. On the one hand, traditional arguments that PE buyouts create

value by mitigating agency conflicts within firms (e.g., Jensen (1989)) do not apply well to

buyouts of private firms, where separation of ownership and control is already limited. On the

other hand, if PE specialists possess professional management skills, these skills should be more

valuable in private firms, many of which are run by founders or their families. In addition,

because private firms have fewer options for raising capital, the financial resources of a PE buyer

could create value by relaxing financing constraints that limit these firms’ growth.1

Our sample includes 263 U.S. private firm buyouts taking place between 1995 and 2009.

We start by examining the determinants of private firm buyouts. PE acquirers are more likely to

buy private firms with weak growth and high leverage (median debt-to-assets is 59.2% in the

1 Changes around private rather than public firm buyouts may also better isolate the direct effects of buyouts

themselves, as private firm buyouts are not accompanied by the elimination of analyst coverage, observable stock

price, and financial disclosure requirements, each of which could have consequences for the acquired firm.

2

year before the buyout in our sample), which could indicate financial or economic distress.

However, buyout firms have slightly better operating performance (as measured by return on

assets) than the average private firm. Among buyout firms unlikely to be distressed (those with

above-industry median operating performance pre-buyout), PE acquirers are more likely to buy

firms in industries with good growth options (implied by high industry median Tobin’s Q) and

low return on assets. This, combined with their slow growth and high debt load, suggests

financing constraints prevent these firms from exercising valuable growth options.

Next, we study how private buyout firms evolve after a buyout. To investigate the

impact of buyouts on operating performance, we examine changes in pre-interest return on sales.

We find that operating profitability improves after a buyout (median increase in pre-interest

return on sales of 1.5%), but only for underperforming firms (those with below-industry median

operating performance pre-buyout). These results hold both in absolute terms and relative to

three benchmarks (including two based on matched samples) designed to account for the effects

of selection, trends, and mean reversion in performance. While ruling out alternative

explanations is difficult, the results are consistent with PE buyers creating value in part by

improving the operational efficiency of struggling private firms.

Our analysis of buyout determinants suggests PE acquirers target at least some firms

facing binding financing constraints. We investigate whether buyouts relax these constraints by

examining changes in sales growth around buyouts. Firms with below average operating

performance pre-buyout experience rapid growth post-buyout. This could indicate a relaxation

of financing constraints, though it could also indicate an increase in growth opportunities as a

result of the improvement in operating margins in these firms. However, we also find that better-

performing firms experience a large increase in sales growth post-buyout. As these firms appear

3

to already have good growth opportunities (as evidenced by their high industry Q), their growth

is more likely driven by a relaxation of financing constraints.2

This does not, however, explain how PE buyouts relax financing constraints in private

firms. To explore the specific channel, we examine equity contributions to acquired firms for a

subperiod in which we have contributions data (2005-2009). With the caveats that this period is

brief and partly overlaps with the financial crisis, a substantial majority of both underperforming

and better-performing firms receive a large contribution in the year of the buyout. About half of

the firms in each group also receive a contribution in the year after the buyout. While such

contributions may be necessary for the survival of firms in the first category, many of which are

likely financially distressed, contributions to those in second category are likely to represent

growth capital injections.3

Finally, we analyze how a firm’s capital structure evolves from before to after a buyout.

Private firms targeted in PE buyouts have high leverage at the time of the buyout but still

increase leverage moderately as a result of the buyout. Median debt-to-assets in the sample

increases from 59.6% the year before the buyout to 70.8% at the end of the buyout year. 4

Moreover, mean and median leverage remain virtually unchanged over at least the first few years

post-buyout, even in firms that generate excess cash flow and therefore have the capacity to pay

down debt. This suggests that the increase in leverage accompanying buyouts is intended, and is

not just a byproduct of using debt financing to minimize agency or other transactions costs

2 The tax return data do not allow us to distinguish between organic internal growth and growth due to add-on

acquisitions. A review of news articles suggests PE acquirers do sometimes use acquired companies as platforms

for acquiring other companies. 3 That PE buyers continue to inject funds even into struggling companies helps explain the findings of Hotchkiss,

Smith, and Strӧmberg (2012) that PE-owned firms tend to avoid financial distress costs. 4 This increase is considerably less than in PE buyouts of public firms. Using the same data, Cohn, Mills, and

Towery (2013) find that median debt-to-assets increases after buyouts of public firms from 36.6% to 74.8% over

approximately the same window.

4

associated with the deal.5 Better-performing firms pre-buyout are less likely to pay taxes post-

buyout and experience more clustering of pre-tax income at or just below zero post-buyout,

consistent with these buyout firms using debt to actively manage tax burden.

In sum, our results suggest that private firm buyouts create value in at least two ways: (i)

by turning around previously struggling firms, and (ii) by relaxing financing constraints that

prevent firms with good growth options from exercising those options. Thus, we provide

evidence that PE buyouts of private firms create value in part through the positive real effects

they have on acquired private firms. One possible implication of our results is that private equity

buyouts are viable substitutes for initial public offerings as a means of raising growth equity for

these firms.

Our study contributes to the private equity literature in several ways. First, ours is the

first paper that we are aware of to analyze the determinants of private firm PE buyouts. Prior

studies of public firm buyouts document a negative relation between growth opportunities and

the likelihood of buyout (Opler and Titman (1993); Cohn, Mills, and Towery (2013) (hereafter,

CMT)). This relation is often interpreted as support for the argument that buyouts reduce

overinvestment in low-growth industries. In contrast, we find a positive association between

growth opportunities and the likelihood of buyout for private-to-private transactions, consistent

with buyouts creating value by relaxing financing constraints and reducing underinvestment.

Second, ours is the first study to present consistent evidence of improvements in

operating profitability for a sample of U.S. PE buyouts in the 1990s and 2000s. Several papers

find evidence of improvements after management buyouts in the 1980s (Kaplan (1989a), Smith

(1990), Smart and Waldfogel (1994)). In contrast, Guo, Hotchkiss, and Song (2011) find mixed

5 One argument for why PE buyers rely on debt financing is that it minimizes costs due to information asymmetries

between the general and limited partners investing in the PE fund (Axelson, Strӧmberg, and Weisbach (2009)).

5

evidence of performance improvements after buyouts in the 1990s and 2000s of public firms that

file public financial statements post-buyout, while CMT find no operating improvements when

looking at almost the entire population of public firm buyouts over a similar period.6 Our results

are more consistent with the findings of Davis et al. (2013), who find improvements in total

factor productivity after PE buyouts occurring between 1980 through 2005 using plant-level

data. Moreover, they find that their results are driven by the divestiture of inefficient plants,

which could explain the concentration of improvements in underperforming firms (those more

likely to have inefficient plants) in our sample.7 Our results could also help explain the positive

excess returns to PE funds (Harris, Jenkinson, and Kaplan (2013)).8

Third, our results complement those of Boucly, Sraer, and Thesmar (2011) and Chung

(2011), who find growth after PE buyouts of private firms in Europe, and Bernstein, Lerner,

Sorensen, and Strӧmberg (2010), who conclude that industries in which more PE capital has

been invested grow faster across many countries.9 Our paper adds to this stream of literature not

only by showing similar effects in the U.S., but also by showing that private PE targets appear

financially-constrained pre-buyout and receive direct equity contributions during and after the

buyout that likely facilitate growth. It also relates more broadly to a recent paper by Erel, Jang,

and Weisbach (2013), who find that acquisitions in general relax financing constraints faced by

the target.

6 CMT do find consistent improvements in performance after public firm buyouts of firms that file public financial

statements post-buyout. However, as these represent firms that were able to subsequently go public again or issue

public debt, they represent an ex post selected sample, making it difficult to extrapolate from these results. 7 Acharya et al. (2011) and Weir, Jones, and Wright (2008) find modest improvements in operating performance

after PE buyouts in the U.K., which has a well-developed market for corporate control. Boucly, Sraer, and Thesmar

(2011) and Bergstrӧm, Grubb, and Jonsson (2007), in contrast, find large improvements in operating performance

after PE buyouts during this period in France and Sweden, respectively, which have less developed markets for

corporate control. 8 There is a literature debating the magnitudes of financial returns to private equity funds, which also includes work

by Ljungqvist and Richardson (2003), Kaplan and Schoar (2005), Phalippou and Gottschalg (2009), and Robinson

and Sensoy (2013a,b). 9 In a related point, Lerner, Sørensen, and Strӧmberg (2011) find that PE-owned firms in the U.S. are at least as

innovative as (if not more so than) non-PE owned firms using data on patents.

6

Finally, our paper contributes to the literature on the evolution of capital structure after

PE buyouts. Consistent with the private buyout firm results in the current paper, CMT find no

evidence of deleveraging post-buyout for public firm buyouts in the U.S. during the 1990s and

2000s. Our results are also consistent with the findings of Kaplan (1991) for PE buyouts

between 1979 and 1986 that firms remaining private at the end of 1989 have leverage ratios

comparable to those immediately after the buyout. Kaplan (1989b), in contrast, finds that firms

taken private in management buyouts (MBOs) in the 1980s pay down approximately 25% of

their debt in the first two years after the MBO.10

The remainder of the paper is organized as follows. Section I describes the sample and

research design used in the paper. In Section II, we analyze the determinants of private-to-

private buyouts. Sections III, IV, and V consider the evolution of operating performance,

growth, and leverage around private-to-private buyouts, respectively. Section VI concludes.

I. Sample and Research Design

A. Sample

To construct our sample, we first identify PE buyouts of private (i.e., not publicly-traded)

stand-alone firms between 1995 and 2009 using Thomson Financial's Securities Data

Corporation (SDC) Platinum Mergers database. Panel A of Table I summarizes our sample

construction. We exclude buyouts of bankrupt firms, partial buyouts, and buyouts of firms with

less than $10 million of assets. This yields an initial sample of 1,504 possible buyouts. We then

hand-collect news articles discussing each of these transactions. Based on these news articles,

we remove buyouts that were never actually completed, misclassified buyouts, buyouts of firms

concurrently merged into other entities, and buyouts of Real Estate Investment Trusts (REITs)

10

See Kaplan and Strӧmberg (2009) for a more complete review of the private equity literature.

7

and partnerships.11

We also eliminate buyouts that we are not able to verify using news articles.

This leaves 683 buyouts, which we then attempt to match with IRS data based on firm name.

We match 555 of these 683 buyouts to the IRS tax return data, a match rate of 81%.

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Insert Table I here

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Our analysis of the determinants of buyouts requires at least two years of pre-buyout IRS

data. We have such data for 292 buyout firms. Our analysis of the consequences of buyouts

requires both pre- and post-buyout financial information from the IRS data. This is complicated

by the fact that the name of the acquired company often changes at the time of the buyout, for

example when the PE buyer creates a holding company to undertake the acquisition. We use

information from news articles to identify the new name of an acquired firm in the event of a

name change if possible.

Panel B shows the number of buyouts for which we have data covering the year prior to

the buyout through a given number of years after the buyout. Much of our analysis on the

consequences of buyouts will focus on tracking a firm from the year before the buyout to the

second year post-buyout. We acknowledge that survivorship bias is naturally a concern with

these analyses. For example, firms disappearing from the sample after the buyout might

disappear because their performance is weak enough that they cease to operate. On the other

hand, firms disappearing may be acquired by other firms because they are successful. However,

most of the buyouts for which we do not have data from the year before the buyout to two years

11

We eliminate firms immediately merged into other operating entities because we cannot make before-to-after

comparisons in these firms. We eliminate buyouts of REITs and partnerships because these firms file different

income tax returns.

8

after the buyout appear to be missing because the firm is transformed from a corporation to a

partnership at the time of the acquisition, and our data includes only corporations. We see no

obvious reason for why the loss of these firms from the sample would induce any biases in our

analysis.

Panel B of Table I also shows that the number of private firm buyouts grows substantially

from 2003 to 2008, before decreasing during the height of the financial crisis in 2009. Of the

555 buyouts in our initial sample, 416 take place between 2004 and 2009 (versus 139 between

1995 and 2003). This is consistent with the increased prominence of private firm buyouts in

recent years.

B. Corporate Tax Return Data

All of the financial variables used in this study are constructed using corporate tax return

data collected by the IRS in its Business Return Transaction File (BRTF). The BRTF contains

items from the first five pages of the U.S. Corporation Income Tax Return Form 1120, including

taxable income statement data (page 1), book balance sheet data (page 5, Schedule L), and some

stockholders’ equity reconciliation and book-tax difference information. 12

These data enable us

to compute standard measures of operating performance and sales growth based on the taxable

income detail (Form 1120, page 1) and interest-bearing debt based on the book balance sheet

(Form 1120, page 5, Schedule L).13

12

Various authors compare financial data to tax return data to estimate tax payments (Lisowsky (2009)), simulated

marginal tax rates (Graham and Mills (2008)), or book-tax differences (Manzon and Plesko, 2002). Although none

of these papers focuses specifically on whether taxable income provides a reasonable measure of operating income,

Manzon and Plesko (2002) conclude that differences between book and taxable income could be estimated

consistently over time. Thus, our cross-time tests should control for consistent differences between book and

taxable income. In addition, CMT compute Pearson (Spearman) correlations between tax return measures and

financial statement measures for publicly-traded firms. The authors find correlations of 56.7% (62.0%) for return on

sales and 65.0% (61.2%) for return on assets. 13

Our measures of operating performance are based on tax reporting definitions of revenues and expenses from U.S.

Corporation Income Tax Return, Form 1120, page 1. As such, they could be affected by tax avoidance incentives.

However, to the extent that leverage is a substituted tax shield (MacKie-Mason (1990); Graham (1996); Dhaliwal,

9

C. Operationalizing Post-transaction Operating Performance, Growth, and Leverage

To study general trends in operating performance, growth, and leverage before and after

the buyout transaction, we adopt an event study approach by lining up the buyout years across

firms. We designate the first tax return filed on or after the buyout completion date as the year t

observation. The Appendix defines each of our variables.

Our primary measure of operating performance is pre-interest return on sales

(PreInterestROS), which equals PreInterestIncome, computed as pretax income for tax purposes

(NetIncome, Form 1120, Line 28) plus the interest deduction (IntDeduction, Form 1120, Line

18), divided by gross receipts (Sales, Form 1120, Line 1e). We use pre-interest income because

we are interested in studying operating performance before financing. PreInterestIncome is

analogous to earnings before interest and taxes (EBIT) as computed from a firm's financial

statements. We focus on PreInterestROS rather than a return on assets-based measure because

of concerns about write-ups and write-downs of reported asset values at the time of a buyout.14

Our conclusions, however, are unchanged if we use pre-interest return on assets.

To analyze growth, we capture sales (Sales) from the tax return data. Our leverage

measure (DebtToAssets) equals interest-bearing liabilities (IntBearingLiab) divided by

TotalAssets, where IntBearingLiab equals short-term and long-term mortgages, notes, and bonds

payable (Form 1120, Schedule L, Lines 17 and 20).15

D. Summary Statistics

Trezevant, and Wang (1992); Cloyd, Limberg, and Robinson (1997); Graham, Lang, and Shackelford (2004), our

buyout firms could have lower incentives to use non-debt tax shields. That our buyout firms are already highly

levered pre-buyout therefore helps mitigate the concern that tax incentives influence our measures of operating

performance. 14

See Guo et al. (2011) and CMT for a thorough discussion of write-ups and write-downs (i.e., basis adjustments). 15

Two caveats are in order with respect to our leverage measure. First, some of a private firm’s debt may be owed

to the owners of the firm, likely in the form of subordinated debentures. To the extent that this debt should be

thought of being effectively equity, our leverage measures will overstate a firm’s true leverage. Second, we do not

observe operating leases, which are not included in debt. However, some have argued that operating leases should

be treated as debt for the purpose of calculating leverage ratios.

10

Table II provides descriptive information for the buyout firms in our sample at year t-1

(pre-buyout) and year t+2 (post-buyout) for the 263 observations for which we have data

covering the t-1 to t+2 window around the buyout. All continuous variables are winsorized at

the extreme 2.5 percent to limit the influence of potential outliers.

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Insert Table II here

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The table indicates that the mean (median) firm in our sample has pre-transaction

TotalAssets of $91.8 ($45.2) million. Not surprisingly, firms in the sample are considerably

smaller than the public buyout firms studied by CMT, which have mean (median) TotalAssets of

$921 ($253) million. The mean (median) firm has DebtToAssets of 58.8% (59.6%) in year t-1.

CMT find mean (median) t-1 DebtToAssets of 44.7% (43.2%) in year t-1 for public-to-private

buyout firms. Thus, private firms acquired by PE buyers appear to be substantially more

leveraged at the time of the buyout than public firms acquired by PE buyers.

II. Determinants of Private Firm Buyouts

We begin our analysis by examining the empirical determinants of private firm buyouts.

Doing so allows us to shed light on which types of private firms PE buyers target, and to

speculate on the motive(s) for these buyouts. We also use the results from this analysis to

construct a matched sample of private firms not acquired in PE buyouts using propensity score

matching in order to provide a benchmark for changes in operating performance and growth

around buyouts.

To predict which publicly-traded firms are acquired by PE buyers, we estimate a logistic

regression model based loosely on Opler and Titman (1993). Because they use Compustat data

11

to compute explanatory variables, and we cannot compute analogous variables from the tax

return data for some of these, we are forced to exclude some of the variables that they use. The

firm-level explanatory variables in our estimation include the natural log of total t-1 assets

(ln(Assets)), the t-1 DebtToAssets, the t-1 pre-interest return on assets (PreInterestROA), and the

percent change in sales from t-2 to t-1 (SalesGrowth). In addition, we include the t-1 median

industry-level Tobin’s Q computed using all publicly-traded firms in Compustat. We cannot use

a firm-specific Tobin’s Q, as the firms in our sample are private and hence do not have an

observable market value. Table III presents the results of these tests for the full sample as well

as for firms with above- and below-industry median PreInterestROS in year t-1.

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Insert Table III here

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The table reveals several interesting facts. First, PE acquirers tend to target private firms

in industries in which Tobin’s Q and the firm’s return on assets are high, but sales growth and

debt-to-assets are low. Jensen (1989) and others argue that PE buyouts of public firms create

value by solving an overinvestment problem arising from moral hazard within the firm.

Consistent with this moral hazard justification for PE buyouts, Opler and Titman (1993) and

CMT (2013) find that PE firms tend to target publicly-traded firms with low Tobin’s Q, where

Tobin’s Q is typically treated as a proxy for the quality of a firm’s growth opportunities.

That private firm targets appear to have good investment options but are growing slowly

suggests that, if anything, these firms are underinvesting. One possible explanation for such

underinvestment is that these firms are financially constrained. Private firms are generally

limited in their financing options, as they cannot easily issue equity and rely mainly on bank

borrowing to finance growth. The fact that private firm targets are highly-leveraged is further

12

evidence in support of the financing constraints argument, as these firms are likely at or near

their financing capacity.16

If PE buyouts of firms alleviate financing constraints, perhaps by

infusing the firm with more capital, these buyouts can create value by solving the

underinvestment problem. Boucly, Sraer, and Thesmar (2011) and Chung (2011), who study

buyouts of private French and UK firms, respectively, also argue that the ability to solve

financing constraints motivates PE buyouts of private firms, though they do not examine the

determinants of buyout decisions.

Interpreting the positive relation between the probability of a buyout and return on assets

is less straightforward. On the one hand, healthier firms likely have better investment

opportunities, and are therefore hurt more by a lack of financing. On the other hand, these firms

likely have higher internal cash flow with which to finance these investment opportunities, and

therefore might be less likely to curtail investment due to a lack of investable resources. We

further explore this issue by estimating the logistic regression separately for firms with above-

industry median and below-industry median PreInterestROS in year t-1 separately.

Column 2 shows that, among better performers, PE buyers again target firms in industries

with higher median Tobin’s Q. However, they also target firms within this group with lower

return on assets. These firms could be especially constrained in their investment, as they likely

possess good investment opportunities (as evidenced by high industry Tobin’s Q) but have

limited internal cash flow. Therefore, in assessing the prediction that private firm buyouts

alleviate financing constraints, we focus on this set of firms.

16

Another explanation for the high pre-buyout leverage of private firm buyout targets is that they have more stable

cash flows and can therefore support more debt. Unfortunately, we do not have a sufficiently long time series to

estimate the variance of each firm’s cash flows over time and hence cannot rule out this possibility. However, the

fact that these firms tend to be in high growth industries suggests that they are less likely to have stable cash flows.

13

The alleviation of financing constraints may be a less important driver of buyouts of

underperforming firms, as the primary motive in these cases may simply be to turn the firm’s

operations around. Column 3 shows that, among underperforming firms, the probability of a

buyout is not related to industry Tobin’s Q and is positively related to return on assets. This

suggests that financing constraints are unlikely to be an important driver of these buyouts. An

alternative motive for buyouts of these firms is that buyout specialists believe that they possess

skill in turning around struggling firms. Indeed, many buyout firms describe turnarounds as a

substantial part of their operations. We explore this possibility in further detail when we

examine changes in operating performance after PE buyouts of private firms in the next section.

III. Operating Performance and Private Firm Buyouts

In this section, we examine the evolution of operating performance around PE buyouts of

private firms. We begin by graphically examining trends in PreInterestROS in the years

surrounding a buyout. One concern with interpreting such trends is that we cannot observe the

counterfactual – what would have happened absent the buyout. Specifically, trends in

performance around the buyout could be explained by the nature of firms private equity acquirers

choose to acquire, industry- or market-level profitability trends, or mean reversion in

profitability. For example, PE acquirers might be skilled at selecting targets that would have

experienced improvements in performance even absent of the buyout. Alternatively, PE

acquirers might implement operational changes that prevent declines in performance that would

otherwise occur. To address the counterfactual issue as best we can, we compare trends in

buyout firm performance to trends in three benchmarks.

14

Our first benchmark is the industry median PreInterestROS of private firms over the

same period. We define industries by 3-digit NAICS codes, which are available in the tax return

data. Comparing performance trends in buyout firms to this benchmark allows us to account for

any skill that PE buyers have in selecting industries that are likely to perform well in the future.

However, it does not account for skill at selecting firms within an industry.

Our second benchmark is based on propensity score matching. For each buyout firm, we

identify the unacquired private firm in the same industry-year with the closest propensity to be

acquired using the logit estimation in the first column of Table III. We then use the median

PreInterestROS within this sample of propensity-matched firms as a benchmark. This

benchmark accounts for several observed factors driving buyout decisions that might also be

related to subsequent trends in performance, though it cannot account for unobserved factors.

Our third benchmark is based on matching firms with similar pre-buyout performance

levels and trends. For each buyout firm, we identify the unacquired private firm in the same

industry-year with the most similar level of PreInterestROS in year t-1 (the year before the

buyout) and change in PreInterestROS from year t-2 to year t-1. We require performance match

firms to have operating performance within 80% and 120% or within -0.01 and 0.01 of the

buyout firm’s operating performance in year t-1.17

We again use the median PreInterestROS

within this sample of performance-matched firms as a benchmark. This third benchmark

accounts for any trends in performance, including those relating to mean reversion, that might

drive changes in profitability after buyouts. However, unlike propensity score matching, this

benchmark does not account for other factors linked to the buyout decision that might also drive

subsequent changes in performance.

17

We define industries using 3-digit NAICS codes. If there are no match firms with the same 3-digit NAICS code,

we relax this criterion and look for match firms with the same 2-digit NAICS code or 1-digit NAICS code.

15

Figure 1 presents trends in median buyout firm PreInterestROS and the three

benchmarks. Panel A presents the trends for the full sample of firms. It shows that

PreInterestROS drops somewhat from year t-2 to year t-1. One possible explanation for this fall

is a changing composition of firms. We do not observe year t-2 PreInterestROS for 15 of the

firms in the sample. However, even if we focus only on the 248 firms for which we do observe

year t-2 PreInterestROS, we find a similar drop in performance from year t-2 to year t-1.

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Insert Figure 1 here

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PreInterestROS increases from year t-1 to years t+1 and beyond. These increases are

substantive. The three benchmarks, in contrast, generally decline slightly around the time of the

buyout, making the relative improvement in PreInterestROS after buyouts even larger. All three

benchmarks are similar to buyout firm PreInterestROS in year t-1. However, in contrast to the

pre-buyout trend in PreInterestROS for buyout firms, the industry median and propensity

matched benchmarks show slight upward trends from year t-2 to year t-1.

In general, this raises concerns that differential pre-existing trends could bias

comparisons of changes in PreInterestROS in buyout firms and changes in these benchmarks

around the buyout (i.e., in differences-in-differences estimation, which we discuss shortly). In

this particular case, as the difference between buyout firm PreInterestROS and these two

benchmarks narrows from year t-2 to year t-1, a continuation of the factors driving these

differential trends would lead to a downward bias in the estimated effect of the buyout. On the

other hand, if performance mean reverts, the downward pre-buyout trend for buyout firms could

be transitory, and we might misattribute improvements in performance post-buyout to the buyout

itself rather than simply to a return to baseline profitability. By construction, the performance

16

matched benchmark does a good job of matching both year t-1 PreInterestROS and the change in

PreInterestROS from year t-2 to year t-1. This benchmark is therefore especially helpful in at

least partially addressing concerns that mean reversion in performance drives the improvements

in observed buyout firm operating performance post-buyout.18

One reason we do not observe larger improvements in performance could be that the

scope for improvement is simply not large in many of the firms in our sample. From Table II, a

slight majority of firms in our sample have PreInterestROS greater than their industry medians.

As we note in Section II, the scope for improvement is likely to be larger for firms that

underperformed pre-buyout (i.e., that are turnaround candidates). We therefore divide the

sample into firms with above- and below-industry median PreInterestROS in year t-1, and plot

the trends in PreInterestROS separately for these two groups in Panels B and C of Figure 1. We

also plot trends in the benchmarks, which we calculate separately using private firms with above-

and below-industry median PreInterestROS, respectively.

Panel B shows that median PreInterestROS increases from year t-2 to t-1 for firms with

above-industry median profitability, and then decreases after the buyout. However,

performance-matched firms show a similar (or even slightly stronger) decline in performance

after year t-1, suggesting that mean reversion could explain the apparent performance decline

after buyouts. The industry median and propensity matched-based benchmarks are both flat

post-buyout. That the industry median and propensity match benchmarks are much lower than

buyout firm PreInterestROS is not surprising, as the buyout firms in Panel B are selected based

on having a high level of PreIntrestROS (relative to industry median).

18

See the recent paper by Roberts and Whited (2012) on endogeneity in corporate finance for an extensive

discussion of the importance of the parallel trends assumption in differences-in-differences estimation.

17

Panel C shows the opposite pattern for firms with below-industry median profitability:

PreInterestROS decreases from year t-2 to t-1, and then increases post-buyout. The trend in

Panel C is consistent with buyouts leading to improvements in performance. The trend is also

positive and strong relative to each of the three benchmarks. While the large swing in buyout

firm performance could also indicate mean reversion in performance, the improvement after the

buyout is much larger than the improvement in the performance-matched benchmark, which

exhibits a similar performance level and trend pre-buyout.

Next, we test whether the changes in performance shown in Figure 1 are statistically

significant over different windows around buyouts. Table IV presents the results of these tests.

---------------------------------------------

Insert Table IV here

---------------------------------------------

Panel A shows changes in PreInterestROS for the full sample of buyout firms. The first

row shows the mean and median raw change in PreInterestROS over windows from year t-1 to

each of the years t+1, t+2, and t+3. Note that the numbers shown here do not perfectly match the

differences in levels over the same windows as shown in Panel A in Figure 1, as those reflect the

change in median PreInterestROS rather than the median change in PreInterestROS.

Nevertheless, the patterns are highly consistent. The mean increase in PreInterestROS is

positive and statistically significant at the one percent level over all three windows. The median

increase is 1.5% from year t-1 to years t+1 and t+2, and is statistically significant at least at the

five percent level in both cases. It falls to 0.7% and loses statistical significance in the t-1 to t+3

window, though we note that we lose 36 observations here because of lack of data for year t+3.

The remaining three rows in Panel A of Table IV show the change in performance

relative to each of the three benchmarks. That is, they show the mean (median) change in

18

performance less the mean (median) change in each benchmark. Industry-adjusting leads to a

slightly larger increase in the performance improvement measured from t-1 to the years

subsequent to the buyout. This indicates that firms in the same industry as the buyout firm

actually experience a slight decline in operating performance relative to the buyout year. Thus, it

does not appear that the observed improvement in operating performance in buyout firms relative

to year t-1 is being driven by industry trends in performance. Propensity-adjusting and

performance -adjusting operating performance changes also lead to slight increases in the change

in PreInterestROS around buyouts relative to year t-1.

While each of our benchmarking approaches addresses some of the issues associated with

the lack of an observed counterfactual, PE buyouts are ultimately not random events. We

therefore cannot rule out the possibility that buyouts on average have an effect that is either

better or worse than what we measure. Nevertheless, the fact that the improvements in

performance relative to year t-1 are robust to comparisons to three different benchmarks is

consistent with PE buyouts of private firms producing detectable improvements in the

performance of these firms. This contrasts with the conclusions of Guo et al. (2011) and CMT

for publicly-traded firms, which show no consistent evidence of improvements in performance

after PE buyouts.

Panels B and C repeat the tests in Panel A for firms with above- and below-industry

median PreInterestROS in year t-1, respectively. The first rows in each show patterns consistent

with those in Panels B and C of Figure 1: Healthier firms experience declines in PreInterestROS

post-buyout, while underperforming firms experience increases in PreInterestROS post-buyout.

For the healthier firms (Panel B), these declines shrink slightly after adjusting for industry trends

in performance or for the change in performance of propensity-matched firms. They disappear

19

completely after adjusting for the change in performance of firms with similar pre-buyout

performance and trend in performance. As the matched firms in this last case were also highly-

profitable pre-buyout, this suggests that the apparent decline may be driven by mean reversion in

performance.

However, the observed increase in PreInterestROS post-buyout for underperforming

firms (Panel C) does not disappear after adjusting for any of the three benchmarks. The fact that

it shrinks only slightly after performance-adjusting and remains statistically significant over all

windows again suggests that mean reversion does not drive this observed performance

improvement. While it is difficult to definitively determine whether PE buyouts cause

performance improvements in previously struggling firms, they do appear to significantly

outperform reasonable benchmarks.

Finally, we explore the drivers of performance changes around PE buyouts of private

firms using multivariate analysis. Specifically, we regress changes in performance on firm,

transaction, and acquirer characteristics. The results are shown in Table V.

---------------------------------------------

Insert Table V here

---------------------------------------------

The dependent variable in each of the first three columns is performance-adjusted change

in PreInterestROS from year t-1 to year t+2.19

The explanatory variables in the first column are

HighPreIntROSInd, which is equal to one if the firm’s pre-interest ROS in year t-1 is above the

industry median and zero otherwise, IndustryMedianTobinsQ, ln(Assets), PrePETransLeverage,

which is debt-to-assets in year t-1, MBOInd, which takes a value of one if the transaction was a

19

In principle, two years post-buyout may be insufficient time to observe changes in performance. The results in

Table IV, however, suggest few differences if we examine changes up to three years post-buyout. We focus on the

year t-1 to year t+2 window in the multivariate analysis to minimize the number of dropped observations, and also

because concerns about survivorship bias are greater when the window is longer.

20

management buyout and zero otherwise, and ln(TargetAge). We construct all of these variables

using the IRS tax return data, except for MBOInd, which is captured from news articles, and

ln(TargetAge), where TargetAge is calculated as the year of the buyout minus the year the firm

was founded as reported by either Bloomberg BusinessWeek’s private firm database or Mergent

Online. The sample size is slightly smaller here than in the tests in Table IV because we cannot

determine TargetAge for a few of the firms in our sample.

The first column shows that change in operating performance around the buyout relative

to performance-matched firms is higher for underperforming firms. This is consistent with the

results in Table IV, and suggests that the univariate results are not driven by any of the other

explanatory variables in the regression. Relative change in performance also increases with firm

size, though we do not offer an explanation for this relation. None of the other coefficients in the

table are statistically significant.

In the second column, we add TurnoverInd to the set of explanatory variables. This

variable takes a value of one if the CEO was replaced at the time of the buyout and zero

otherwise. It is possible that PE buyouts improve performance in part by eliminating

underperforming CEOs, suggesting that improvements might be greater in cases where the CEO

is replaced. We determine whether the CEO was replaced from news articles describing the

buyout if this information is available. It is only available for about half of the buyouts in our

sample, substantially reducing the sample size in the second column.

Relative change in performance continues to be higher for underperforming firms and for

larger firms. It does not appear to vary depending on whether the CEO was replaced or not. Of

course, the effect of a CEO on performance could outlast her tenure. A firm experiencing

turnover due to CEO incompetence may have been destined for a large decline in performance

21

absent separation from the CEO, which that separation averts. Thus, while we find no evidence

that performance is better in cases where there is CEO turnover, we cannot rule out the

possibility that replacing the CEO has a positive effect on buyout firm performance.

In the third column, we remove TurnoverInd but add three characteristics of the

acquirer(s). ClubDealInd is equal to one if there are multiple PE acquirers and zero otherwise.

AcquirerAge is the age of the acquirer, calculated as the difference between the buyout year and

the founding year of the PE sponsor as reported by Bloomberg BusinessWeek’s private firm

database or Mergent Online. The BusinessWeek Bloomberg database also contains a brief

description of the investment approach of the PE firm. The variable AcquirerTurnaroundInd is

equal to one if this description includes the words “turnaround,” “restructuring,” or

“bankruptcy,” and is intended to capture whether the acquirer is a turnaround specialist. If there

are multiple acquirers, we compute AcquirerAge and AcquirerTurnaroundInd as the average of

these variables across all acquirers. The sample size shrinks slightly, as we are not able to locate

all acquirers in the BusinessWeek Bloomberg data and Mergent Online data, and in a few cases

relevant data items are missing.

As the third column shows, none of the acquirer variables are statistically significant. It

is somewhat surprising that improvements in performance are not greater when the acquirer is a

turnaround specialist. While this may be due to noise in the turnaround specialist variable, it

could also indicate that the nature of the firm is more important than the nature of the buyer in

driving changes in performance post-buyout. The coefficient on HighPreIntROSInd remains

negative and statistically significant at the one percent level. The coefficient on ln(Assets) ceases

to be statistically significant.

22

One concern with the tests in the first three columns is that, despite the fact that we

winsorize PreInterestROS at the 2.5th

and 97.5th

percentiles, relative change in PreInterestROS,

the dependent variable in the regressions, has a few outliers. To address this problem, we

compute the percentile of the relative change in PreInterestROS within the sample. We then

rerun the tests in the first three columns of Table IV using this as the dependent variable. The

results are reported in the final three columns of the table.

The coefficient on HighPreIntROSInd continues to be negative and statistically

significant in all three columns, suggesting that the tendency of firms underperforming pre-

buyout to experience larger improvements in operating performance is robust. The coefficient

on ln(Assets) ceases to be statistically significant. The coefficient on ln(TargetAge) is now

positive and statistically significant in all three columns. Conversations with private equity

executives suggest that older firms are often excessively conservatively-managed and therefore

tend to be good turnaround targets.

Overall, this section provides evidence of operating performance improvements

following private firm buyouts, which are concentrated in cases where the scope for

improvement is the largest.

IV. Growth and Private Firm Buyouts

The evidence in the previous section showed modest improvements in performance

around PE buyouts of private firms, with improvements concentrated in firms that

underperformed pre-buyout. These improvements, however, might understate the impact a PE

buyout has on a firm’s operations if the firm is able to grow substantially faster as a result of the

buyout than it otherwise would have been able to grow. Given the fairly high post-buyout

23

profitability of firms in our sample, increased growth could create substantial value. This section

examines growth in sales around PE buyouts of private firms.

We begin by examining the trends in Sales around the time of the buyout. We also show

trends in industry median sales, as well as sales for the two matched samples described in the

previous section, as benchmarks. The trends are shown in Figure 2.

---------------------------------------------

Insert Figure 2 here

---------------------------------------------

The figure follows the same format as Figure 1. Panels A, B, and C show trends for all

firms in the sample, firms with above-industry median pre-buyout PreInterestROS, and firms

with below-industry median pre-buyout PreInterestROS, respectively. Panel A shows that sales

tend to decline from year t-2 to year t-1 for the full sample, and then to grow rapidly in the first

three years after the buyout both in absolute terms and relative to each of the three benchmarks.

Panels B and C show that the decline in sales from t-2 to t-1 is concentrated in firms with below-

industry median PreInterestROS, providing further evidence that these firms are struggling pre-

buyout. The figure also shows that firms with both above- and below-industry median

PreInterestROS grow rapidly post-buyout, again both in absolute terms and relative to

benchmarks. This suggests that post-buyout growth does not just reflect the turnaround of

underperforming firms, but represents even healthy firms expanding. Any trends pre-buyout in

either buyout firm sales or the benchmarks appear mild.

In Table VI, we test whether the apparent growth of firms after buyouts represents a

statistically significant increase in scale. The format of this table is the same as that of Table IV.

---------------------------------------------

Insert Table VI here

---------------------------------------------

24

Each cell in the table shows the mean or median percentage change in sales relative to

year t-1. Again, the numbers here do not perfectly match the differences in levels over the same

windows in Figure 2, as one captures the mean or median sales growth, while the other captures

the change in median sales. The first row of Panel A confirms that private firms acquired by PE

buyers grow substantially post-buyout, with a median increase in sales of 52.9% from year t-1 to

year t+2. The second row shows that these firms do not grow faster than the industry median

growth rate, suggesting that they might have grown quickly even absent the buyout just due to

industry trends. However, the third and fourth rows show that these firms do grow relative to

performance- and propensity-matched firms from the same industry. Thus, while PE targets are

in fast-growing industries, they are most similar on observable dimensions to firms within those

industries that are slow-growing relative to the industry as a whole.

Panels B and C show roughly similar patterns for firms with above- and below-industry

median operating performance pre-buyout. Poorly-performing firms grow more rapidly than

better-performing firms in absolute terms and relative to performance-matched firms, but more

slowly relative to industry median growth and propensity-matched firms. Thus it is unclear

which of the two groups experiences a larger increase in growth in conjunction with the buyout.

Nevertheless, both groups appear to experience substantial growth post-buyout. If one interprets

the results in Table III as evidence that PE buyers target financially-constrained firms because

they can create value by relaxing the constraint and increasing growth, the results in Figure 2 and

Table VI suggest that they succeed in doing so.20

20

We cannot determine whether the growth in private firms after PE buyouts reflects internal growth or growth by

acquisitions. Indeed, we find some indication in reading news articles that PE buyers sometimes buy a firm to use

as a “platform company,” which then subsequently rolls up other firms in the same line of business. We eliminate

observations from our sample in which the acquired firm is immediately merged with another firm. This includes

cases where a PE buyer acquires multiple firms simultaneously and merges them. Regardless, it is unclear how

much the implications of our results would change if growth were driven by acquisitions rather than generated

internally, as both require investment that a financially-constrained firm is likely unable to make.

25

We further explore the drivers of sales growth after PE buyouts of private firms using

multivariate analysis. Specifically, we regress the change in Sales from year t-1 to year t+2 on

firm, transaction, and acquirer characteristics. The results are shown in Table VII.

---------------------------------------------

Insert Table VII here

---------------------------------------------

We employ the same set of explanatory variables and same specifications as in Table V,

though we replace AcquirerTurnaroundInd with AcquirerGrowthInd, which is equal to one if the

PE acquirer’s BusinessWeek Bloomberg description includes the word “growth” and zero

otherwise. With AcquirerGrowthInd, we are attempting to capture the fact that some PE firms

specialize in providing “growth capital,” capital intended to allow a portfolio company to finance

growth. The dependent variable is percentage sales growth from year t-1 to year t+2 relative to

performance-matched firms in the first three columns, and the percentile of this variable within

the sample in the final three columns.

The coefficient on HighPreInterestROSInd is negative and fairly large in all six columns,

though it is only statistically significant in four of those columns. Thus, we find some evidence

that post-buyout growth is greater in firms that were underperforming pre-buyout. It is

noteworthy that the coefficient on AcquirerGrowthInd is positive in columns three and six, and is

statistically significant at the five percent level in column six. This provides some evidence that

acquired firms do in fact grow faster when acquired by a PE firm that specializes in the growth

capital. In general, though, we observe few strong patterns across any of the explanatory

variables other than pre-buyout performance. This suggests that the improvement in

performance in struggling firms is a fairly general phenomenon.

26

V. Financial Policy and Private Firm Buyouts

This section investigates the evolution of a firm’s financial structure and policies around

the time it is acquired by a PE buyer. To further explore the possibility that PE buyouts of

private firms relax financing constraints and facilitate growth, we first examine equity

contributions to PE-acquired firms. We then consider how capital structure changes at the time

of and after a buyout. Finally, we examine how acquired firms’ tax burdens change after

buyouts.

A. Equity Contributions and Private Firm Buyouts

If PE buyouts of private firms alleviate financing constraints, it must be because they

make additional capital available to these firms. One natural source of capital is the PE buyer

itself, which can inject capital into the firm in conjunction with the buyout as well as in

subsequent years. We therefore analyze equity contributions to private firms that PE buyers

acquire. We compute equity contributions in a given year as the increase in paid-in capital

(Form 1120, Schedule L) from the previous year. Unfortunately, the IRS data include paid-in

capital only in 2005 and later. However, this period does incorporate the financial crisis, a

period when firms are more likely to face cash shortfalls and therefore are more likely to benefit

from capital injections.

Table VIII presents our data on contributions. Panels A, B, and C show contributions

scaled by total assets for the years t-1 through t+3 for all firms in our sample, those with above-

industry median PreInterestROS, and those with below-industry median PreInterestROS,

respectively.

---------------------------------------------

Insert Table VIII here

---------------------------------------------

27

As Panel A shows, equity injections in the year of the buyout are substantial. The median

contribution is 3.6% of total assets (vs. 0% the year before the buyout). Contributions are also

highly-skewed. The 75th

percentile is 51.8% of total assets (vs. 0.3% the year before the buyout).

While some of these equity contributions might take place after the buyout is complete, it is

likely that much of it represents capital injected into the company at the time of the buyout. Not

surprisingly, equity contributions in the years after the buyout are smaller. However, they are

still substantial in many cases. While the unconditional median contribution is only 0.1% in year

t+1 and 0% in the years after year t+1, the 75th

percentile is 3.7% of assets in year t+1 and 1.7%

in year t+2.

Mean equity contributions in the year of the buyout are substantially larger in firms with

below-industry median PreInterestROS in year t-1. This is consistent with underperforming

firms having accumulated large financing deficits that need to be filled. However, more than

50% of firms with above-industry median PreInterestROS receive contributions in each of the

first two years. Indeed, a larger percentage of healthier firms receive contributions post-buyout

than underperforming firms, though the size of contributions to the former are smaller on

average. This provides evidence of a specific channel through which PE buyouts of private firms

relax financing constraints and facilitate growth, and is further evidence that the ability to relax

financing constraints is a motive for such buyouts.

B. Capital Structure and Private Firm Buyouts

PE buyers typically rely heavily on leverage in financing buyouts. While there is debate

about the reason for this reliance, the result is usually a substantially increase in the acquired

firm’s leverage. Thus, PE buyouts are often synonymous with leveraged buyouts (LBOs). One

important question is whether PE-acquired firms pay down debt in the post-buyout period or

28

maintain the newly-elevated level of leverage. This question is important because it sheds light

on the rationale for the reliance on debt. If PE acquirers rely on debt financing because it

minimizes transaction costs, then we would expect to see PE-bought firms pay down debt if they

generate excess cash flow post-buyout. However, if they rely on debt financing because they

view higher leverage as optimal, then the acquired firm should not reduce debt in the post-buyout

period.

We begin by simply examining the trends in DebtToAssets in the years around PE

buyouts of private firms. These trends are shown in Figure 3.

---------------------------------------------

Insert Figure 3 here

---------------------------------------------

Panel A shows the trend for the full sample. We first note that private firms acquired in

PE buyouts are often highly-leveraged at the time of the buyout. Median DebtToAssets in year t-

1 is 59.2%. This is consistent with the positive coefficient on DebtToAssets in the logistic

regression in Table III predicting which private firms are acquired in PE buyouts. Pre-buyout

leverage in private firms acquired by PE buyers is substantially higher than for public firms

acquired by PE buyers. Specifically, CMT find median year t-1 DebtToAssets for a sample of

public buyout targets of 43.2% using the same tax return data.

Post-buyout leverage, on the other hand, is slightly lower for private PE buyout targets

than for public buyout PE targets. Median year t DebtToAssets is 71.9% for private firms

acquired by PE buyers. The comparable number from CMT for public firms acquired by PE

buyers is 75.4%. Thus the amount of additional leverage that a private firm takes on when it is

acquired by a PE buyer is, on average, substantially lower than the amount that a public firm

takes on. We see similar patterns if we look at means rather than medians.

29

Figure 3 also shows that leverage does not decrease, and if anything appears to increase,

in the years after the buyout relative to its level at the first year-end after the buyout is complete.

This suggests that the change in leverage accompanying private firm buyouts is long-lived. This

is consistent with PE buyers consciously recalibrating the capital structure of the firms they

acquire towards a higher degree of leverage.

Panels B and C show trends in DebtToAssets for firms with above- and below-industry

median PreInterestROS, respectively. Not surprisingly, less profitable firms have higher

leverage pre-buyout. This gap goes away at the end of the buyout year (year t), and leverage if

anything continues to grow slightly post-buyout for both groups.

In Table IX, we test whether the change in leverage from year t (i.e., immediately after

the buyout) to subsequent years (t+2 through t+5) is statistically significant.

---------------------------------------------

Insert Table IX here

---------------------------------------------

Panel A shows the results for the full sample. The first row shows that mean increase in

DebtToAssets from year t to years t+3, t+4, and t+5 is positive and statistically significant.

Median increases are also positive, but are smaller and not always statistically significant. Even

though leverage ratios do not fall in the post-buyout period, these firms could still be reducing

their debt levels if their total assets are also decreasing. However, the second row of Panel A

shows that debt as a percentage of buyout year-end debt (DebtAs%ofYearTDebt) does not

decrease, and if anything increases as well. Patterns are similar for firms with above- and below

industry median year t-1 PreInterestROS (Panels B and C, respectively).

One possible reason that buyout firms do not reduce leverage is that they do not generate

sufficient cash flow to pay off debt. We therefore also analyze post-buyout leverage changes

30

separately for firms with and without excess free cash flow in the years after the buyout. To do

so, we construct a FreeCashFlow measure equal to NetIncome in years t+1 and t+2 plus an

estimate of depreciation minus an estimate of capital expenditures in these years. Because our

tax return data exclude capital expenditures and include only the depreciation in operating

expense but not the depreciation included in cost of goods sold, we use industry median capital

expenditures and depreciation from Compustat to estimate these amounts.21

We refer to firms

with positive FreeCashFlow as ExcessFreeCashFlow firms and those with negative

FreeCashFlow as ShortfallFreeCashFlow firms.

We examine the change in debt-to-assets around private firm buyouts for both of these

groups of firms in Panels D and E of Table IX. Panel D shows that even firms that appear to

generate excess cash flow, and therefore likely have the capacity to reduce leverage, do not do so

either in the short-run or the long-run after the buyout. Panels E shows that firms with a cash

flow shortfall also do not reduce leverage post-buyout.

Note that the increase in DebtToAssets and DebtAs%ofYearTDebt we are measuring here

is purely in the post-buyout period. That is, it does not include the increase in leverage that

accompanies the buyout itself, which would be reflected in the change from year t-1 to year t.

Note also that the number of observations declines as we look further out from the time of the

buyout itself. Thus, concerns about survivorship bias become a significant issue over the longer

windows. Ultimately, though, the results here clearly do not indicate that private firms acquired

21

Specifically, we compute median depreciation and median capital expenditures in years t+1 and t+2 from

Compustat for all firms in the same three-digit NAICS industry in the same years. We then divide median

depreciation and capital expenditures by median property, plant, and equipment for the industry in the same years to

get year t+1 and t+2 industry depreciation and capital expenditure rates. We then calculate our firm-level estimate

of depreciation and capital expenditures for each of the two years by multiplying these rates by firm-level property,

plant, and equipment as reported in the tax return data for the same years.

31

by PE buyers pay down debt in the years after the buyout. This suggests that the increase in

leverage accompanying the buyout is intentional.

C. Tax Burden and Private Firm Buyouts

We next investigate the effect of PE buyouts on the tax burdens of the acquired firms.

One rationale for the use of leverage in PE buyouts is the tax deductibility of interest expense.

The resulting tax shields increase the amount of cash flow available for the firm’s financial

claimants. We begin by simply plotting the percentage of firms in our sample that actually pay

federal corporate taxes in each year relative to the buyout year. The results are shown in Figure

4.

---------------------------------------------

Insert Figure 4 here

---------------------------------------------

Again, Panel A shows the trend in taxpayer status for all firms in the sample, while

Panels B and C show the trends for firms with above- and below-industry median

PreInterestROS in year t-1. As Panel A shows, despite the increase in debt that accompanies PE

buyouts of private firms (see Figure 3), the percentage of these firms paying taxes does not

change considerably around buyouts. 55.9% of the firms in our sample pay taxes in year t-1,

while 53.2% pay taxes in year t+2. The additional interest tax shields due to the higher level of

debt are largely offset by the additional profits that firms generate post-buyout (see Figure 1 and

Table IV).

Panel B shows that there is a considerable drop in the percentage of firms with high year

t-1 operating performance paying taxes starting in year t. This is not surprising, as these firms

experience larger increases in debt than less profitable buyout firms, and no increase in

profitability on average. Panel C shows the opposite for firms with low year t-1 operating

32

profits. Only 23.2% of these firms pay taxes in year t-1, while 43.2% pay taxes in year t. Thus,

the large increase in the operating profitability of these firms more than offsets the increase in tax

shields due to the increase in debt.

The results overall do not suggest that PE buyers are aggressive about minimizing tax

burdens when they buy private firms. This contrasts with buyouts of public firms, where the

fraction of firms paying taxes drops considerably and remains low for many years afterwards

(CMT). One possible explanation for the relative conservatism with private firm buyouts is that

these firms have a lot of growth options, making the costs of financial distress high.

We further explore how PE buyers manage the tax burden of their portfolio companies by

examining the distribution of pre-tax income scaled by total assets both before and after buyouts.

The results are shown in Figure 5.

---------------------------------------------

Insert Figure 5 here

---------------------------------------------

Panel A shows the distribution for all firms in the sample. If a large part of the objective

of buyouts is to minimize taxes, we should observe an increase in the clustering around or just

below 0% pre-tax income from pre- to post-buyout. We do not observe that here. In fact, the

percentage of firms with pre-tax income that is between -5% and -1% or between -1% and 1% of

total assets falls slightly from year t-1 to year t+2. This contrasts with the findings of CMT for

public firm buyouts. Clustering around zero pre-tax income increases substantially after these

buyouts, suggesting that tax minimization is an objective in buyouts of public firms. The

increase in the percentage of firms with pre-tax income greater than 10% or less than -10% of

total assets in Figure 5 suggests that the variability of earnings grows after private firm buyouts.

While some firms experience large improvements in operating income post-buyout and therefore

33

increases in pre-tax income, others do not, and the increase in interest expense as a result of the

increased debt in the buyout reduces their pre-tax income.

Panels B and C show the same distributions for firms with above- and below-industry

median year t-1 PreInterestROS, respectively. Panel B shows a marked increase in the

percentage of firms with pre-tax income between -5% and -1% and between -1% and 1% of total

assets from pre- to post-buyout. This suggests that tax minimization may be an objective of

buyouts of healthy private firms. These firms have considerable pre-tax earnings to shield from

taxation pre-buyout, with over 80% having pre-tax income greater than 1% of total assets in year

t-1.

Panel C shows a drop in the percentage of firms with pre-tax income between -5% and -

1% and between -1% and 1%, with a substantial increase in the percentage with pre-tax income

greater than 1%. This suggests that the improvement in operational performance that these firms

experience dominates the higher level of interest expense due to the increase in debt. It seems

plausible that the value that can be created by improving operational performance in these firms

would be a higher priority than tax minimization.

VI. Conclusion

This paper uses U.S. corporate tax return data to study private firm buyouts between 1995

and 2009. Our empirical findings shed light on the motives for these buyouts, the consequences

for the acquired firms, and how PE owners manage them as portfolio companies. They also

allow us to compare the evolution of private firms acquired by PE investors with that of public

firms acquired by PE investors as studied in previous papers.

Overall, our results suggest that the purpose and consequences of private firm buyouts are

very different than those of public firm buyouts. Unlike the public firms, private firms acquired

34

in PE buyouts experience substantial operational changes post-buyout, both in terms of operating

performance and growth. Also, while many have argued that public firm buyouts are aimed at

solving an overinvestment problem, our results suggest that private firm buyouts may create

value in part by solving an underinvestment problems. While more research is needed to form

strong conclusions, our paper suggests that PE firms may play a role in positively reshaping

private firms, which in turn could explain the outsized financial returns that investors in PE funds

receive.

35

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38

Appendix

Variable Definitions

NetIncome = Net income reported on Line 28 of Form 1120

IntDeduction = Interest deduction reported on Line 18 of Form 1120

PreInterestIncome = NetIncome plus IntDeduction

IntBearingLiab =Short-term and long-term mortgages, notes, and bonds payable reported on

Lines 17 and 20 of Form 1120 Schedule L

TotalAssets = Total assets reported on Line 15 of Form 1120 Schedule L

DebtToAssets IntBearingLiab divided by TotalAssets

Sales = Gross receipts or sales reported on Line 1e of Form 1120

PosTaxPdInd = one if total tax reported on Line 31 of Form 1120 is positive and zero otherwise

PreInterestROS = PreInterestIncome divided by Sales

PreInterestROA =

PreInterestIncome divided by lagged TotalAssets , although pre-LBO

PreInterestIncome is divided by year t TotalAssets to mitigate the influence of

basis adjustments.

FreeCashFlow =NetIncome in years t +1 and t +2 plus estimated depreciation in years t +1 and

t +2 minus estimated capital expenditures in years t +1 and t +2

DebtAs%ofYearTDebt = IntBearingLiab in year t +k divided by IntBearingLiab in year t

ExcessCFIndicator = one if FreeCashFlow is positive and zero otherwise

MedianIndustryTobinsQ = Industry [market value of assets divided by the book value of assets

(Compustat AT), where the market value of assets equals the book value of debt

(Compustat LT) plus the market value of equity (Compustat PRCC_F *

Compustat CSHO)]SalesGrowth = One-year percentage change in Sales

39

Figure 1

Trend in pre-interest ROS This figure presents trends in median PreInterestROS for private buyout firms in the tax return

data from at least year t-1 through year t+2, where year t is the buyout year. Panels A, B, and C

show the trend for the full sample, firms with above-industry median PreInterestROS in year t-1

(HighIndROS firms), and firms with below-industry median PreInterestROS in year t-1

(LowIndROS firms), respectively. Each panel shows the trend for buyout firms as well as three

benchmarks. The “Industry” benchmark is the median PreInterestROS for firms in the same

industry. The “Prop Match” and “Perf Match” benchmarks are median PreInterestROS for

samples of firms matched based on propensity scoring, and pre-buyout level and trend in

PreInterestROS, respectively.

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

0.080

t-2 t-1 t t+1 t+2 t+3

Pre

-In

tere

st R

OS

Panel A: Trend in median pre-interest ROS for all buyout firms

Buyout Firm

Industry

Prop Match

Perf Match

0.000

0.020

0.040

0.060

0.080

0.100

0.120

t-2 t-1 t t+1 t+2 t+3

Pre

-In

tere

st R

OS

Panel B: Trend in median pre-interest ROS for HighIndROS firms

Buyout Firm

Industry

Prop Match

Perf Match

40

-0.020

-0.010

0.000

0.010

0.020

0.030

0.040

0.050

0.060

0.070

t-2 t-1 t t+1 t+2 t+3

Pre

-In

tere

st R

OS

Panel C: Trend in median pre-interest ROS for LowIndROS firms

Buyout Firm

Industry

Prop Match

Perf Match

41

Figure 2

Trend in Sales This figure presents trends in median Sales for private buyout firms in the tax return data from at

least year t-1 through year t+2, where year t is the buyout year. Panels A, B, and C show the

trend for the full sample, firms with above-industry median PreInterestROS in year t-1

(HighIndROS firms), and firms with below-industry median PreInterestROS in year t-1

(LowIndROS firms), respectively. Each panel shows the trend for buyout firms as well as three

benchmarks. The “Industry” benchmark is the median Sales for firms in the same industry. The

“Prop Match” and “Perf Match” benchmarks are median Sales for samples of firms matched

based on propensity scoring, and pre-buyout level and trend in Sales, respectively.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

t-2 t-1 t t+1 t+2 t+3

Sa

les

($M

)

Panel A: Trend in Sales for all buyout firms

Buyout Firm

Industry

Prop Match

Perf Match

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

t-2 t-1 t t+1 t+2 t+3

Sa

les

($M

)

Panel B: Trend in Sales for HighIndROS firms

Buyout Firm

Industry

Prop Match

Perf Match

42

0.00

20.00

40.00

60.00

80.00

100.00

120.00

t-2 t-1 t t+1 t+2 t+3

Sa

les

($M

)Panel C: Trend in Sales for LowIndROS firms

Buyout Firm

Industry

Prop Match

Perf Match

43

Figure 3

Trend in Debt-to-Assets This figure presents trends in median Debt-to-Assets for private buyout firms in the tax return

data from at least year t-1 through year t+2, where year t is the buyout year. Panels A, B, and C

show the trend for the full sample, firms with above-industry median PreInterestROS in year t-1

(HighIndROS firms), and firms with below-industry median PreInterestROS in year t-1

(LowIndROS firms), respectively.

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

t-2 t-1 t t+1 t+2 t+3

Deb

t-to

-Ass

ets

Panel A: Trend in Debt-to-Assets for all buyout firms

Q1

Median

Q3

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

t-2 t-1 t t+1 t+2 t+3

Deb

t-to

-Ass

ets

Panel B: Trend in Debt-to-Assets for HighIndROS buyout

firms

Q1

Median

Q3

44

0.000

0.200

0.400

0.600

0.800

1.000

1.200

t-2 t-1 t t+1 t+2 t+3

Deb

t-to

-Ass

ets

Panel C: Trend in Debt-to-Assets for LowIndROS buyout

firms

Q1

Median

Q3

45

Figure 4

Trend in positive taxes paid indicator This figure presents trends in the percentage of private buyout firms paying taxes for firms in the

tax return data from at least year t-1 through year t+2, where year t is the buyout year. It shows

these percentages for the full sample, firms with above-industry median PreInterestROS in year

t-1 (HighIndROS firms), and firms with below-industry median PreInterestROS in year t-1

(LowIndROS firms).

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

t-1 t t+1 t+2 t+3

Po

sTa

xP

dIn

d

Trend in positive taxes paid indicator

All firms

HighIndROS Firms

LowIndROS Firms

46

Figure 5

Distribution of Net Income This figure presents the distribution of net income scaled by total assets for private buyout firms

in the tax return data from at least year t-1 through year t+2, where year t is the buyout year.

Panels A, B, and C show the trend for the full sample, firms with above-industry median

PreInterestROS in year t-1 (HighIndROS firms), and firms with below-industry median

PreInterestROS in year t-1 (LowIndROS firms), respectively.

0

0.05

0.1

0.15

0.2

0.25

0.3

<-10% -10% to

-5%

-5% to -

1%

-1% to

1%

1% to

5%

5% to

10%

>10%

% o

f B

uy

ou

t F

irm

s

Scaled Net Income

Panel A: Scaled Net Income for All Firms

t-1

t+1

t+2

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

<-10% -10% to

-5%

-5% to -

1%

-1% to

1%

1% to

5%

5% to

10%

>10%

% o

f B

uy

ou

t F

irm

s

Scaled Net Income

Panel B: Scaled Net Income for HighIndROS Firms

t-1

t+1

t+2

47

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

<-10% -10% to

-5%

-5% to -

1%

-1% to

1%

1% to

5%

5% to

10%

>10%

% o

f B

uy

ou

t F

irm

s

Scaled Net Income

Panel C: Scaled Net Income for LowIndROS Firms

t-1

t+1

t+2

48

Number of PE buyouts of private non-bankrupt firms in SDC from 1995 to 2009 with >=$10M assets 1,504

Less: Misclassified buyouts, REIT and partnership buyouts, buyout firms merged into other entities (656)

Less: PE transactions that could not be verified (165)

Number of PE buyout firms to be matched with IRS data 683

Less: PE transactions not found in tax return data (128)

Initial sample 555

Less: PE transactions not having at least 1 year (2 years) of pre- (post-) transaction data (292)

Final sample 263

Initial

sample

t-1 to

t+2

t-1 to

t+3

t-1 to

t+4

t-1 to

t+5

1995 19 4 4 4 4

1996 13 7 7 6 5

1997 17 11 10 10 8

1998 17 5 4 4 4

1999 8 7 6 6 6

2000 31 19 16 16 15

2001 13 9 8 7 7

2002 5 3 3 3 3

2003 16 8 8 7 6

2004 42 24 23 22 21

2005 57 35 31 29 27

2006 85 38 34 31 25

2007 83 38 36 34 9

2008 104 42 33 13 0

2009 45 13 6 1 1

555 263 229 193 141

Panel B: PE Transactions by year

Table I

Sample

This table presents the sample derivation. Panel A provides the aggregate number of PE transactions, and Panel B

presents the number of PE transactions by year.

Panel A: Aggregate PE transactions

49

N Mean SD Q1 Median Q3 Mean SD Q1 Median Q3

PreInterestIncome 263 4.0 12.0 -0.3 2.8 6.8 11.3 19.0 1.7 5.6 14.5

IntDeduction 263 3.0 4.7 0.2 1.0 3.8 9.8 13.6 2.1 4.6 10.9

DebtToAssets 263 0.599 0.468 0.293 0.592 0.813 0.744 0.361 0.535 0.772 0.902

IntBearingLiab 263 56.9 90.5 9.7 25.7 53.4 163.7 250.7 28.2 65.1 179.5

TotalAssets 263 91.8 122.1 24.7 45.2 107.5 223.4 332.0 50.6 92.1 218.4

Sales 263 99.1 132.6 27.0 53.2 116.9 165.9 186.6 46.8 87.3 204.0

PosTaxPdInd 263 0.559 0.497 0.000 1.000 1.000 0.532 0.500 0.000 1.000 1.000

PreInterestROS 263 0.008 0.190 -0.007 0.050 0.108 0.065 0.101 0.023 0.066 0.123

PreInterestROA 263 0.085 0.262 -0.008 0.072 0.160 0.071 0.124 0.021 0.068 0.114

N Mean SD Q1 Median Q3 Mean SD Q1 Median Q3

PreInterestIncome 138 9.8 11.6 3.3 5.5 10.6 13.4 21.2 2.6 6.6 18.1

IntDeduction 138 3.1 4.8 0.2 1.1 3.8 10.2 15.1 2.0 4.2 11.0

DebtToAssets 138 0.578 0.462 0.269 0.578 0.809 0.724 0.399 0.516 0.757 0.884

IntBearingLiab 138 54.2 96.8 10.0 23.0 46.9 165.1 280.5 26.8 58.8 137.8

TotalAssets 138 94.2 135.4 22.7 40.3 99.1 233.5 365.2 50.6 87.6 214.7

Sales 138 106.4 142.4 31.4 52.3 117.7 169.5 204.7 44.8 76.0 196.0

PosTaxPdInd 138 0.855 0.353 1.000 1.000 1.000 0.623 0.486 0.000 1.000 1.000

PreInterestROS 138 0.117 0.064 0.068 0.104 0.146 0.077 0.102 0.034 0.078 0.135

PreInterestROA 138 0.202 0.285 0.097 0.145 0.226 0.095 0.134 0.035 0.081 0.137

N Mean SD Q1 Median Q3 Mean SD Q1 Median Q3

PreInterestIncome 125 -2.4 8.9 -5.0 -0.4 1.4 8.9 16.0 0.9 4.9 12.8

IntDeduction 125 2.9 4.5 0.2 0.8 3.8 9.4 11.7 2.1 5.9 10.6

DebtToAssets 125 0.622 0.475 0.350 0.642 0.813 0.766 0.315 0.540 0.802 0.915

IntBearingLiab 125 59.8 83.2 9.4 28.6 69.8 162.1 214.1 31.6 88.2 196.6

TotalAssets 125 89.3 106.1 25.7 54.4 109.1 212.2 292.0 50.8 119.1 241.0

Sales 125 91.1 120.9 21.0 55.1 115.2 162.0 165.0 50.3 96.2 204.0

PosTaxPdInd 125 0.232 0.424 0.000 0.000 0.000 0.432 0.497 0.000 0.000 1.000

PreInterestROS 125 -0.112 0.210 -0.189 -0.015 0.016 0.052 0.098 0.009 0.057 0.107

PreInterestROA 125 -0.044 0.152 -0.073 -0.013 0.027 0.045 0.107 0.010 0.057 0.090

Panel C: PE transactions with below-median Industry PreInterestROS

Post-buyout (year t+2)

Table II

Descriptive statistics

This table presents descriptive statistics for the sample. Panel A provides the descriptive statistics measured at

year t-1 and at year t+2 for all PE transactions. Panel B (C) provides the descriptive statistics measured at year t-1

and at year t+2 for high profit (low profit) PE transactions. Year t represents the PE buyout year.

Panel A: All PE transactions

Pre-buyout (year t-1)

Panel B: PE transactions with above-median Industry PreInterestROS

50

Variable

Intercept -3.7359 *** -3.5854 *** -3.6523 *** -3.5144 *** -3.6361 *** -3.3948 ***

(-34.72) (-22.77) (-25.53) (-16.66) (-20.78) (-12.97)

MedianIndustryTobinsQ 0.2506 *** 0.1654 ** 0.3646 *** 0.2881 *** 0.1054 -0.0347

(5.23) (2.03) (5.78) (2.69) (1.34) (-0.25)

DebtToAssets 0.3899 *** 0.117 0.4705 *** 0.2179 0.3624 *** -0.0691

(8.22) (0.54) (7.65) (0.74) (4.48) (-0.19)

MedianIndustryTobinsQ 0.1519 0.1391 0.2443

* Leverage (1.29) (0.88) (1.21)

ln(Assets ) 0.0311 ** 0.0314 ** 0.0045 0.0047 0.0417 * 0.0425 *

(2.2) (2.23) (0.25) (0.26) (1.72) (1.76)

PreInterestROA 1.0145 *** 1.0231 *** -0.6131 * -0.6123 * 2.9209 *** 2.9446 ***

(5.15) (5.19) (-1.92) (-1.91) (7.01) (7.06)

SalesGrowth -0.8563 *** -0.8583 *** -1.1091 *** -1.1092 *** -0.3931 *** -0.3999 ***

(-9.92) (-9.94) (-9.69) (-9.69) (-2.98) (-3.03)

Number of buyout obs. 292 292 191 191 101 101

Number of non-buyout obs. 174,388 174,388 87,617 87,617 86,771 86,771

Pseudo R-squared 0.0459 0.0463 0.0786 0.0788 0.0419 0.0428

Table III

Likelihood of undergoing a PE buyout

This table presents the results from the probit regression model estimating the likelihood of undergoing a PE

buyout. Asterisks *, **, *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. z-

statistics are reported in parentheses. See Appendix for variable definitions.

DV: BuyoutInd

All buyout firms

Above-median Industry

PreInterestROS firms

Below-median Industry

PreInterestROS firms

51

Mean 0.060 *** 0.057 *** 0.063 ***

Median 0.015 ** 0.015 *** 0.007

N 263 263 227

Mean 0.070 *** 0.069 *** 0.076 ***

Median 0.024 *** 0.021 *** 0.021 ***

N 259 259 224

Mean 0.095 *** 0.149 *** 0.143 ***

Median 0.034 *** 0.030 0.030 **

N 248 248 175

Mean 0.025 * 0.057 *** 0.043 *

Median 0.019 *** 0.031 *** 0.022

N 248 248 178

Mean -0.029 ** -0.037 *** -0.035 ***

Median -0.022 *** -0.014 -0.030 ***

N 138 138 124

Mean -0.025 ** -0.031 *** -0.027 **

Median -0.020 ** -0.010 * -0.023 **

N 138 138 124

Mean -0.020 0.024 -0.016

Median -0.009 -0.036 *** -0.016

N 127 127 91

Mean -0.006 0.009 -0.024

Median 0.004 0.014 * 0.001

N 127 127 98

Mean 0.158 *** 0.161 *** 0.181 ***

Median 0.066 *** 0.077 *** 0.088 ***

N 125 125 103

Mean 0.178 *** 0.184 *** 0.204 ***

Median 0.080 *** 0.107 *** 0.116 ***

N 121 121 100

Mean 0.215 *** 0.279 *** 0.316 ***

Median 0.077 *** 0.107 *** 0.124 ***

N 121 121 84

Mean 0.056 ** 0.108 *** 0.125 ***

Median 0.041 *** 0.049 *** 0.069 **

N 121 121 80

Panel C: Change in PreInterestROS for Below-median Industry PreInterestROS firms

Performance-

Adjusted

Propensity-

Adjusted

Performance-

Adjusted

Propensity-

Adjusted

Unadjusted

Industry-

Adjusted

t-1 to t+1

Table IV

Univariate tests of performance

This table tests for changes in operating performance using t-tests and Wilcoxon rank tests. Year t represents the

PE buyout year. Asterisks *, **, *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively.

See Appendix for variable definitions.

Panel A: Change in PreInterestROS for all firms

t-1 to t+3

Unadjusted

Industry-

Adjusted

t-1 to t+1 t-1 to t+2

t-1 to t+2

Performance-

Adjusted

t-1 to t+1 t-1 to t+2

t-1 to t+3

Panel B: Change in PreInterestROS for Above-median Industry PreInterestROS firms

Propensity-

Adjusted

t-1 to t+3

Unadjusted

Industry-

Adjusted

52

HighPreIntROSInd -0.1288 *** -0.1834 *** -0.1224 *** -0.0853 ** -0.1474 ** -0.107 **

(0.035) (0.056) (0.038) (0.04) (0.058) (0.046)

IndustryMedianTobinsQ -0.0173 -0.0011 -0.0933 -0.1339 -0.1565 -0.1855

(0.086) (0.12) (0.097) (0.098) (0.126) (0.114)

ln(Assets ) 0.0345 ** 0.0457 ** 0.0201 0.0016 0.0255 -0.0114

(0.016) (0.022) (0.017) (0.018) (0.024) (0.02)

PrePETransLeverage -0.0158 0.0256 -0.0128 -0.0766 * -0.0509 -0.0774

(0.036) (0.053) (0.04) (0.042) (0.057) (0.048)

MBOInd -0.0012 -0.0154 0.0239 0.0416 0.032 0.0797

(0.044) (0.06) (0.052) (0.052) (0.066) (0.06)

ln(TargetAge ) 0.0313 0.0336 0.0219 0.049 ** 0.0797 *** 0.0443 *

(0.02) (0.028) (0.021) (0.022) (0.028) (0.024)

TurnoverInd 0.0071 -0.0216

(0.061) (0.064)

AcquirerAge 0.0007 0.001

(0.001) (0.001)

ClubDealInd -0.0056 -0.0215

(0.061) (0.072)

AcquirerTurnaroundInd -0.0072 0.0028

(0.037) (0.044)

Year fixed effects Yes Yes Yes Yes Yes Yes

N 234 121 195 248 131 208

R-squared 0.175 0.3069 0.1623 0.1994 0.3653 0.2268

Table V

Multivariate tests of operating performance

This table presents multivariate results for changes in operating performance after a private equity buyout transaction. Asterisks *, **,

*** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are reported in parentheses. See

Appendix for variable definitions.

DV: Perf-Adj ChgPreInterestROS from t-1 to t+2 DV: Rank Perf-Adj ChgPreInterestROS from t-1 to t+2

53

Mean 1.446 *** 1.762 *** 2.111 ***

Median 0.416 *** 0.529 *** 0.647 ***

N 263 263 229

Mean 0.102 -0.336 -0.354

Median -0.049 -0.003 0.103

N 259 259 226

Mean 0.496 ** 0.865 *** 1.121 **

Median 0.148 *** 0.224 *** 0.255 ***

N 248 248 177

Mean 0.971 *** 1.106 *** 0.740 *

Median 0.253 *** 0.332 *** 0.326 ***

N 248 248 186

Mean 0.827 *** 1.010 *** 1.179 ***

Median 0.256 *** 0.415 *** 0.471 ***

N 138 138 124

Mean 0.316 0.087 0.163

Median 0.030 0.150 ** 0.211 ***

N 138 138 124

Mean 0.689 ** 1.238 *** 1.492 **

Median 0.185 *** 0.330 *** 0.418 ***

N 127 127 91

Mean 0.422 * 0.241 -0.033

Median 0.223 ** 0.244 * 0.265 **

N 127 127 104

Mean 2.129 *** 2.592 *** 3.213 ***

Median 0.788 *** 0.887 *** 1.103 ***

N 125 125 105

Mean -0.141 -0.818 * -0.983

Median -0.259 -0.394 ** -0.453

N 121 121 102

Mean 0.294 0.474 0.728

Median 0.110 0.034 0.069

N 121 121 86

Mean 1.548 *** 2.013 *** 1.720 **

Median 0.382 *** 0.505 *** 0.538 ***

N 121 121 82

Industry-

Adjusted

t-1 to t+1 t-1 to t+3

Table VI

Univariate tests of growth

This table tests for changes in sales using t-tests and Wilcoxon rank tests. Year t represents the PE buyout year.

Asterisks *, **, *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. See Appendix for

variable definitions.

Panel A: Salesgrowth for all firms

Unadjusted

t-1 to t+2

Unadjusted

Performance-

Adjusted

Unadjusted

Industry-

Adjusted

Propensity-

Adjusted

Propensity-

Adjusted

Panel C: Salesgrowth for Below-median Industry PreInterestROS firms

t-1 to t+1 t-1 to t+2 t-1 to t+3

t-1 to t+3

Performance-

Adjusted

Propensity-

Adjusted

Industry-

Adjusted

Performance-

Adjusted

Panel B: Salesgrowth for Above-median Industry PreInterestROS firms

t-1 to t+1 t-1 to t+2

54

HighPreIntROSInd -1.9436 ** -2.7335 ** -1.5517 * -0.0783 * -0.1019 -0.0515

(0.796) (1.34) (0.866) (0.041) (0.063) (0.047)

IndustryMedianTobinsQ -1.7082 -3.6129 -3.3659 -0.0868 -0.268 * -0.1401

(1.957) (2.889) (2.189) (0.101) (0.137) (0.115)

ln(Assets ) -0.1204 -0.5872 -0.3062 -0.0226 -0.0338 -0.0246

(0.366) (0.529) (0.396) (0.019) (0.026) (0.02)

PrePETransLeverage 0.1816 1.1534 -0.1103 -0.0569 -0.004 -0.0889 *

(0.817) (1.267) (0.904) (0.043) (0.062) (0.049)

MBOInd 1.6337 2.4015 * 1.6771 0.0363 0.0983 0.0468

(1.011) (1.433) (1.165) (0.053) (0.072) (0.061)

ln(TargetAge ) 0.3448 0.1436 0.1229 0.0458 ** 0.0544 * 0.0401

(0.45) (0.67) (0.47) (0.023) (0.031) (0.025)

TurnoverInd -1.7753 -0.0532

(1.463) (0.071)

AcquirerAge 0.0127 0.0013

(0.024) (0.001)

ClubDealInd 2.0564 -0.0242

(1.37) (0.073)

AcquirerGrowthInd 0.6037 0.1016 **

(0.948) (0.05)

Year fixed effects Yes Yes Yes Yes Yes Yes

N 234 121 195 248 131 208

R-squared 0.1282 0.2381 0.1615 0.1475 0.2624 0.1816

Table VII

Multivariate tests of growth

This table presents multivariate results for changes in sales after a private equity buyout transaction. Asterisks *, **, *** denote two-

tailed statistical significance at 10%, 5%, and 1%, respectively. Standard errors are reported in parentheses. See Appendix for variable

definitions.

DV: Perf-Adj SalesGrowth from t-1 to t+2 DV: Rank Perf-Adj SalesGrowth from t-1 to t+2

55

N Mean SD Q1 Median Q3

Contributions t-1 72 -0.079 0.507 0.000 0.000 0.003

Contributions t 72 0.384 1.084 0.000 0.036 0.518

Contributions t+1 72 0.110 0.327 0.000 0.001 0.037

Contributions t+2 72 0.203 0.880 0.000 0.000 0.017

Contributions t+3 38 0.174 1.379 0.000 0.000 0.008

N Mean SD Q1 Median Q3

Contributions t-1 30 -0.013 0.105 0.000 0.000 0.002

Contributions t 30 0.274 0.660 0.000 0.024 0.498

Contributions t+1 30 0.087 0.233 0.000 0.001 0.028

Contributions t+2 30 0.061 0.258 0.000 0.002 0.015

Contributions t+3 15 0.004 0.014 0.000 0.005 0.015

N Mean SD Q1 Median Q3

Contributions t-1 42 -0.127 0.657 0.000 0.000 0.016

Contributions t 42 0.464 1.308 0.000 0.090 0.663

Contributions t+1 42 0.126 0.382 0.000 0.000 0.103

Contributions t+2 42 0.305 1.127 0.000 0.000 0.038

Contributions t+3 23 0.284 1.780 0.000 0.000 0.001

Table VIII

Trends in equity contributions

This table presents trends in equity contributions scaled by pre-PE transaction assets from year t-1 to year t+3.

Year t represents the PE transaction year.

Panel C: Trend in equity contributions for Below-median Industry PreInterestROS firms

Panel A: Trend in equity contributions for all firms

Panel B: Trend in equity contributions for Above-median Industry PreInterestROS firms

56

Mean 0.073 *** 0.143 *** 0.135 *** 0.171 ***

Median 0.013 0.048 ** 0.042 0.067 **

N 263 229 193 141

Mean 0.186 *** 0.445 *** 0.415 *** 0.616 ***

Median 0.007 0.070 * 0.038 0.074 *

N 261 227 191 139

Mean 0.037 0.115 *** 0.077 0.146 *

Median -0.006 0.035 0.055 0.030

N 138 124 103 83

Mean 0.168 *** 0.450 *** 0.387 *** 0.684 ***

Median -0.009 0.044 0.064 0.034

N 136 122 101 81

Mean 0.112 *** 0.177 *** 0.201 *** 0.208 **

Median 0.041 * 0.076 * 0.033 0.111 **

N 125 105 90 58

Mean 0.205 *** 0.440 *** 0.445 *** 0.521 *

Median 0.040 0.080 0.020 0.142 **

N 125 105 90 58

Mean 0.058 * 0.132 *** 0.099 0.151 *

Median 0.023 0.097 ** 0.068 0.129 **

N 92 83 78 61

Mean 0.205 *** 0.406 *** 0.422 ** 0.478 **

Median 0.062 0.156 ** 0.089 0.182 *

N 90 81 76 59

Mean 0.080 *** 0.150 *** 0.159 *** 0.187 **

Median 0.001 0.035 0.018 0.032

N 171 146 115 80

Mean 0.176 *** 0.468 *** 0.410 *** 0.718 ***

Median -0.003 0.035 0.016 0.040

N 171 146 115 80

DebtToAssets

DebtAs%

OfYearTDebt

DebtToAssets

DebtAs%

OfYearTDebt

Panel E: Change in capital structure for ShortfallFreeCashFlow firms

t to t+2 t to t+3 t to t+4 t to t+5

Panel D: Change in capital structure for ExcessFreeCashFlow firms

t to t+2 t to t+3 t to t+4 t to t+5

Table IX

Univariate tests of capital structure

This table tests for changes in capital structure using t-tests and Wilcoxon rank tests. Year t represents the PE

buyout year. Asterisks *, **, *** denote two-tailed statistical significance at 10%, 5%, and 1%, respectively. See

Appendix for variable definitions.

Panel A: Change in capital structure for all firms

t to t+2 t to t+3

t to t+4 t to t+5

t to t+5t to t+4

DebtToAssets

Panel C: Change in capital structure for Below-median Industry PreInterestROS firms

DebtToAssets

DebtAs%

OfYearTDebt

DebtAs%

OfYearTDebt

t to t+2 t to t+3

Panel B: Change in capital structure for Above-median Industry PreInterestROS firms

DebtAs%

OfYearTDebt

t to t+2 t to t+3 t to t+4

DebtToAssets

t to t+5