Do Going Private Transactions Affect Plant

Productivity?∗

Sreedhar Bharath†, Amy Dittmar‡, and Jagadeesh Sivadasan§

April 2013

Abstract

We examine whether constraints on public firms such as capital market myopia and agency

problems impact firms’ efficiency by testing whether going private improves plant-level productivity

relative to peer control groups. We find no evidence that this is the case. Our key finding is that

while there is evidence for substantial within-plant increases in productivity after going private,

there is little evidence of efficiency gains relative to peer groups of plants constructed to control

for industry, age, size, past productivity and the endogeneity of the going private decision effects.

Also, we do not find evidence that myopic markets lead to under-investment at the plant level. On

the contrary, we find that after going private, firms shrink capital and employment, and sell and

close plants more quickly, relative to peer groups. Our findings cast doubt on the view that public

markets cause listed firms to make sub-optimal, productivity-decreasing choices, or under-invest at

the plant level.

Keywords: Going private, delisting, productivity, efficiency, firm performance, investment

JEL classification codes: G34, G14, G32, D24, D22

∗The research in this document was conducted while the authors were Census Bureau research associates at the

Michigan Census Research Data Centers. Research results and conclusions expressed are those of the authors, and do not

necessarily indicate concurrence by the Bureau of the Census. The results presented here have been screened to ensure

that no confidential data are revealed. We thank Clint Carter and Arnold Reznik for prompt processing of our disclosure

and data access requests, Natarajan Balasubramanian for advice and help with the data, and Xiaoyang Li for research

assistance. All remaining errors are our own.†sbharath@asu.edu, Arizona State University‡adittmar@umich.edu, University of Michigan§jagadees@umich.edu, University of Michigan

1

1 Introduction

The stock market oriented U.S. financial system is often criticized for providing strong incentives to

corporate managers to behave in a myopic manner. Porter (1992) argues that the U.S. system ad-

vances the goals of shareholders interested in near-term appreciation of their shares even at the expense

of long-term performance of American companies. The nexus of stock market analysts, traders and

fund managers increases the focus on quarterly earnings and other short-term metrics of performance.

Further, managers of public firms are subject to agency conflicts and may engage in empire building

(Jensen (1986) and Jensen and Meckling (1976)) or exacerbate the myopic behavior to improve their

own labor market prospects or compensation, which may be tied to the stock price (Rumelt (1987),

Campbell and Marino (1994), Narayanan (1985) and Holmstrom and Ricart i Costa (1986)).

In this paper, we match data on going private transactions to rich plant-level US Census micro-

data to examine how going private affects plant-level productivity, investment and exit (both sale and

closure of plants). We do this to determine whether changes, if any, are consistent with the view that

agency costs and market myopia impact behavior of listed firms more than private firms. If potential

agency costs and capital market myopia associated with being public affect operational performance,

we expect productivity to improve after firms go private.

Perhaps the most widely cited evidence about harmful short-term market pressures come from a

survey of corporate CFOs published in Graham, Harvey and Rajagopal (2005). In this survey, the

authors find that managers would rather take economic actions that would have negative long term

consequences and sacrifice value in order to meet short term quarterly earnings benchmarks. However,

though they can be very informative, surveys measure beliefs, which may not always coincide with

the actions of managers. Indeed, Shleifer and Vishny (1997) in their review of corporate governance

conclude that the theories and arguments in favor of the view that U.S. companies are relatively short

sighted are remarkably short of empirical support. We focus our analysis on the effect of going private

because, as a private firm, the agency conflict that results from dispersed ownership is alleviated and

the influence of a myopic capital market is removed. We therefore examine changes in the plant-level

efficiency of firms that opted out of public markets by going private and contrast these firms’ plants

against a peer group of ’matched’ plants that did not undergo a similar change from public to private.

We find no evidence that going private causes plants to improve their productivity relative to

their matched peers. Specifically, while there is evidence of substantial within-plant (establishment)

increases in productivity after going private (5.1% in the preferred Blundell-Bond TFP measure and

7.2% in labor productivity in the long-run), there is little evidence (close to zero) of difference-in-

difference efficiency gains relative to a peer groups constructed to control for industry, size, and age.1

1We use a widely used measure of labor productivity (defined as real output by employment), and two measures of total

2

Further, our productivity results hold when we exclude all plants from both the going private and

matched control samples that underwent a change in ownership after the going private date, ad-

dressing the potential concern that matched control plants may be undergoing improvements through

changes in ownership.

These results continue to hold when we address potential endogeneity concerns by creating our

matched control sample using past plant productivity or the propensity to go private. To measure the

propensity to go private, we use information for all the firms at the time of their IPO (which is on

average 13 years before the going private decision). In doing so, we build on Bharath and Dittmar

(2010), which investigates the determinants of going private and find that both the characteristics

at the time of the IPO as well as how firm characteristics change over time impact the decision to

go private. Specifically, Bharath and Dittmar show that firm characteristics at the time of the IPO

predict the ultimate decision to go private with a 71% accuracy rate.2 We therefore use their model

to determine the propensity of a firm to go private. The propensity as well as the past productivity

matched results are similar to those from using the industry-age-size matched control group. None of

our findings suggest that operational efficiency of establishments that went private are differentially

enhanced even six years after going private, thus casting doubt on the view that the U.S. stock mar-

ket’s excessive focus on short term results is affecting their long-run operating performance.

Next, we examine investment decisions as well as establishment exits. Most models of market

myopia (Stein (1989)) and also some empirical work (Bhojraj et. al.(2009)) suggest that stock mar-

ket short-termism could lead managers to boost current earnings, at the expense of forgoing longer

term investments. The key problem is that if short-run earnings are poor, the market is unable to

determine whether this is caused by poor management or by prudent long term investments. The mar-

ket partly uses current earnings to forecast future earnings and knowing this, the managers attempt

to manipulate the information available to shareholders by increasing current earnings, potentially at

the expense of long-term investments, resulting in under-investment. Even if the markets were to

assume no myopia on the part of managers, the latter have an incentive to deviate from non-myopic

behavior in order to fool the market by manipulating current earnings. Manipulation in earnings could

be accounting actions or even real actions (such as forgoing investments). To test this implication

factor productivity (defined as a residual from regressing log real output on all logged inputs) in our analysis. All measures

give similar (close to zero) results in the alternative difference-in-differences analysis, suggesting that the peer group changes

for these measures were similar to that for the going private sample. In the descriptive before-after analysis, labor productivity

shows larger increases than TFP measures because it does not adjust for the larger increases in capital (relative to labor).

The alternative measures are discussed in detail in Section 2.2.2Because Bharath and Dittmar’s propensity model examines the probability of listed firms to be taken private, the propensity

matching approach restricts the control group to establishments of publicly listed firms; so the comparison in this case is

between establishments that go from public to private vs establishments that strictly remained public.

3

of myopic behavior, we examine establishment-level capital stock, employment and plant closures. If

market myopia leads to under-investment when the firms are listed, we would expect to see a relative

increase in capital stock, commensurate increases in employment, and a greater patience with (po-

tentially short-term) under-performance and hence a relatively lower propensity to exit (close or sell)

plants, after going private.

We find no evidence that firms increase establishment-level investment after going private. In fact,

relative to the three control groups discussed above, we find that, if anything, firms shrink capital

stock by 15% and employment by 3.3% in the long-run the baseline ’Difference-in-Differences’ analysis

after going private. We further find that going private firms exit (sell or close) plants more quickly

(15.3% higher hazard) than the matched control group, particularly in the short-run after going private.

The higher exit is driven by both greater sales (33% higher hazard) and closures (6.1% higher hazard).

Taken together, the results on establishment level capital stock and exit propensity do not suggest

under-investment by going-private firms while they are public relative to after going private. Some

models of market myopia (Bebchuk and Stole (1993) and Bizjak, Brickley and Coles (1993)) as well

as those of agency problems (Jensen (1986)) predict “over-investment” by public firms. Bebchuk and

Stole (1993) and Bizjak, Brickley and Coles (1993) point out that the relationship between managerial

short-term objectives, imperfect information and firm investment behavior can result in either under-

or over- investment and depends on the observability of investment. For our purposes, the key im-

plication of these models is that the capital market’s fixation with meeting short-term expectations,

coupled with asymmetric information problems, too often hinders corporate managers from focusing

on long-term value creation. Severing the link between capital markets and firms (by going private) will

therefore solve the managerial myopia problem. We should then expect to see a (long term) increase

in productivity due to managers focussing on the value enhancing long term projects. While we do

find that firms appear to downsize after going private, the fact that we find no difference-in-differences

changes in productivity between the public and private firm establishments suggests that myopia is

not impacting the operational performance of listed firms. In other words, the productivity results rule

out both“under” and “over” investment induced by market myopia insofar as these terms are defined

in the context of optimal operational efficiency. Our study contributes to this literature by examining

if there are operational improvements to be obtained by removing the hypothesized myopic pressures

imposed by capital markets; thus, comparing firms when they are public to when they are private.3

Next, we test whether going private firms are more patient with lower performing plants after

de-listing. On the contrary, we find moderate evidence that going private firms more aggressively

3The lack of improvement in productivity also argues against one explanation for downsizing after going private – empire

building due to more severe agency problems in public firms. At the least, our results rule out a negative effect of empire

building on operational efficiency.

4

exit (both sell and close) plants that have lower productivity. Specifically, we find that the signs on

all of the estimates suggest stronger selection to exit lower productive plants, with majority of the

estimates being both statistically and economically significant. Thus, the going private firms appear

to attempt to improve the productivity of their portfolio of plants, but this is achieved through exiting

low productivity plants rather than through improvements.

We interpret our results as suggesting that: (i) myopia or agency problems associated with cap-

ital markets do not affect operational efficiency; (ii) investors in going private transactions appear

to gain value not by improving productivity within target plants, but by reducing capital, as well as

selling and closing (less productive) plants. To explore potential alternative explanations of our results,

we examine differences across different types of acquirers involved in the going private transactions.

Specifically, we classify transactions into three sub-groups – private equity firms, management, and

private operating firms – by collecting data on the parties that drive each of these transactions. We

then compare changes in these groups relative to an industry-age-initial size matched control group

of establishments. One alternative explanation is that high leverage used in going private transactions

could lead to short-termism after delisting, and at the same time exert pressure to downsize plants as

well. Because higher leverage is more likely for management buy-outs and private equity takeovers,

examining the outcomes for operating acquisitions allows us to consider a sub-sample where high

leverage is less of a concern. Another alternative explanation is that (even in transactions without

much debt) for some investors, their time horizon for recouping investment in going private transac-

tions may be short, so that short-termism persists after going private. This is less likely to be true for

operating firm buy-outs.

Our general finding is that regardless of the mechanism used to go private, there is no improvement

in productivity relative to the matched control group.4 In particular, operating firm takeovers, which

constitute about 45% of our sample and which are less likely to be affected by leverage and short-

term investment horizons, do not show improvements in productivity. We also find that operating

firms show large (6.8% to 7.9% in the short-run, 8.3% to 11.6% in the long-run) declines in capital

but smaller and not statistically significant declines in employment. Further, operating firms show a

significantly higher propensity than matched controls to both sell and close establishments; in fact,

over a 6 year horizon, operating acquirers are more likely to close a plant (1.7%) relative to matched

controls than either private equity (-0.6%) or management acquirers (-3.3%), though the propensity

to sell is higher for management and private equity acquirers (6.4% and 7.2% relative to 3.7% for

operating acquirers). Thus, we find that qualitatively the results for the operating firm sample mirror

what we find for the overall sample in the baseline regressions, suggesting that leverage or investor

4One small exception is that using one of our three baseline measures of productivity (Blundell-Bond TFP), we find firms

that go private by being bought by an operating firm have a significant improvement once we purge sold plants from sample.

However, this result is not robust to other TFP measures and is no longer significant once we match by past-productivity.

5

short-termism is unlikely to be the main explanation for the baseline results.

We perform a number of checks and additional analysis to determine the robustness of our results.

We analyze outcomes at the acquiring firm to investigate if changes at the acquiring firm offset, and

hence affect the interpretation of, results for the going private establishments. In particular, we find

no difference-in-differences increases in capital or employment, or productivity measures at acquiring

firm establishments, and also no increase in new establishment openings at acquiring firms. Thus,

we conclude that there is no evidence for changes at the acquiring firm establishments offsetting (or

complicating interpretation of) changes documented at the going-private establishments. In addition

to this, we use profitability (instead of productivity) measures as alternative performance measures,

explore changes in outcomes over different time periods, and check robustness to using alternative

specifications.

Our paper contributes to the long literature that examines productivity changes around corporate

events. Maksimovic, Phillips and Prabhala (2011) and Maksimovic and Phillips (2001) examine pro-

ductivity changes and purchase decisions by acquirers after mergers and acquisitions to understand

how firms redraw their boundaries and evaluate the efficiency of resource reallocation. Maksimovic

and Phillips (2002 and 2008) find that plants acquired by conglomerate firms increase in productivity

and conclude that organizational forms’ comparative advantages differ across industry conditions. Our

paper has a similar approach but focuses on public to private transactions. Lichtenberg and Siegel

(1991) find that TFP (total factor productivity) increases after a leveraged buy out (LBO) using a

sample of 131 firms that conducted an LBO in 1983-1986. Kaplan (1989a, 1989b, and 1991) also ex-

amines the benefits of going private using a sample of LBOs and highlights the importance of tax and

incentive improvements due to the high leverage in these transactions. Our paper expands on these

studies and our sample of firms includes LBOs and MBOs but has a large number of private operating

firms buying public firms and taking them private. Davis et. al. (2008a and 2008b) study changes

after a private equity deal and point out that productivity and employment relationships uncovered in

earlier studies may not hold because of the tremendous changes in the private equity industry due to

increased competition for transactions. They also observe that fundraising (inflation -adjusted dollars)

by U.S. private equity groups is 100 times greater in 2006 than in 1985 and is a primary driver of

these changes. Davis et al (2008a and 2008b) focus on the entire universe of private equity firm deals,

the vast majority of which are private to private transactions. Alternatively, our study focuses only

on public-to-private transactions of manufacturing firms including those acquired by private operating

firms, and studies TFP and employment changes. Based on public 13E-3 filings with the SEC, public

to private deals account for only about 157 out of more than 5,000 deals done by private equity firms

from 1980-2005. Thus, we examine the impact of market myopia by focusing on going private deals

and they investigate the role of private equity.

6

This paper also contributes to an understanding of the impact of capital market myopia on firms

investment and productivity. Both survey and empirical evidence seem to be consistent with the view

of managerial myopia induced by capital markets. Graham, Harvey and Rajagopal (2005) report in

a survey of more than 400 financial executives, that 80 percent of the respondents indicated that

they would decrease discretionary spending on such areas as research and development, advertising,

maintenance, and hiring in order to meet short-term earnings targets and more than 55 percent said

they would delay new projects, even if it meant a small sacrifice in value creation. Assuming these

survey responses reflect the actual intent and behavior of executives, these results indicate that myopia

is a larger issue than companies simply using accounting actions to meet quarterly earnings expec-

tations from financial markets. There are also real actions such as asset sales and forgone strategic

investments that corporate managers use to meet the forecasted quarterly earnings number. In a re-

lated empirical study, Bhojraj et al (2009) document that using accruals or discretionary expenditures

(such as R&D expenditure) to meet or beat analyst forecasts results in short-term positive impact

on firm performance, but long-term under-performance relative to firms that do not manage earnings

to meet forecasts. These results confirm managerial myopia due to capital market pressures in an

empirical setting if one further assumes that firms that manage earnings are most likely to engage in

myopic behavior relative to their control sample that do not manage earnings. Although the creation

of long-term company value is widely accepted as management’s primary responsibility, these results

suggest that managing predominantly for the market’s short-term earnings expectations often impairs

a manager’s ability to deliver value. Asker, Farre-Mensa and Ljungvist (2012) compare a set of small,

private firms with data available on Sageworks with a matched sample of similarly small listed firms.

For this sample, the authors find that listed firms invest less and are less responsive to changes in

investment opportunities compared to matched private firms. Our study differs from these others in

that we examine the change in productivity and investment in labor and capital of firms that go private

relative to a matched sample. By employing the Census data, we are able to both examine a large set

of firms and calculate the impact of going private on their productivity, and we provide evidence that

is inconsistent with public firms being more subject to myopia.

The remainder of the paper is organized as follows. Section 2 describes the data and productivity

measures. In Section 3 we discuss methodology and present results on productivity changes. Section

4 discusses the analyses and results from examining other outcomes (capital, employment, and plant

exit). Section 5 discusses the results and related additional robustness checks. Section 6 concludes.

7

2 Data and productivity measures

2.1 Data sources and description

The main sources of data used in this study are the Census of Manufactures (CMF) for the years 1977,

1982, 1987, 1992, 1997 and 2002, and the Annual Survey of Manufactures (ASM) for remaining be-

tween census years from 1978 to 2004. This census data provide detailed information on individual

establishments (i.e., plants) of both public and private firms. This data has been used in previous

studies, particularly to study the effects of mergers and acquisitions on productivity, discussed earlier

in this paper. We also use the longitudinal business database (LBD) to obtain identifiers to link

establishments over time, and for data used in exit analysis.5 Technical details on the cleaning of the

data, as well as the detailed definitions of the key variables used in the study are provided in the data

appendix. Detailed descriptions of the productivity variables are provided in the next section (with

additional details provided in the data appendix).

To analyze the effect of going-private on firm productivity and other outcome measures, we use a

comprehensive sample of firms that went private as detailed in Bharath and Dittmar (2010). Bharath

and Dittmar (2010) use all forms of 13e-3 filings to identify going private transactions and require

that firms are no longer registered or traded (even over the counter). They also supplement their

sample with data from Lehn and Poulsen (1989) to ensure a complete sample in the early periods.

We then match these firms to census databases using the Compustat-SSEL bridge available at the

Census using 6-digit CUSIP identifers for the period 1981 to 2005 to identify all establishments owned

by the sample of going private firms. In the baseline analysis we create a control sample for each

establishment in the going private sample (hereafter ‘going private establishment’), by including up to

eight establishments (based on data availability) that are closest in size (measured using employment)

to the going-private establishment in the going-private year, from within the same 3-digit SIC industry,

and belonging to the same age quartile. We use this matched sample throughout but also provide

evidence using alternative matched samples by industry, past productivity, age as well as one based

on propensity score matching on the probability a firm will go private.

Panel A of Table 1 presents the summary statistics on the number of establishments in event time

for a period of thirteen years with year 0 being the date of going private. Overall, we have 28,518 go-

ing private establishment year and 157,391 control firm establishment year observations, representing

5,676 going private and 90,009 control sample firm years in the sample. All of our analysis examines

outcomes at the establishment level.6

5We also check if the employment and exit results for manufacturing firms hold for the full universe of firms using data

from the LBD (see point v in section 5).6The first step of the data setup process identifies a baseline sample of going private establishments and matched controls

in year -1. Because of attrition (due to closure or due to not yet being born), the number of establishments is lower in years

8

While the establishment level changes provide a detailed and disaggregated picture of the effects

of going-private, aggregating to the firm level is difficult here because the firm identifier for the estab-

lishments of the acquired firm will change to that of the acquiring entity after the going-private event,

which is not our focus here. Importantly, McGuckin and Nguyen (1995) show how aggregating to the

firm level could mask interesting establishment level changes at the target establishments. There are

two additional reasons to focus on establishments. One, many of the firms have multiple establish-

ments operating in multiple industries; thus, forming a suitable control group for a firm that matches

the firm’s industry composition is more difficult. Two, one aspect of firm behavior we specifically

want to look at is the decision to sell or shutdown particular establishments (see section 4.2). The

establishment level analysis could mask improvements that occur due to selective closure of inefficient

establishments; we address this separately in Section 4.2.3.

Table 1 Panel B breaks down the number of firms and establishments by type of acquirer, i.e. the

type of firm that took the firm private. Of the 11,255 (20,114) going private firms (establishments),

112 (819) were take private by management, 115 (958) by a private equity firm, and 188 (1,207) by a

prior operating firm, similar to the breakdown in Bharath and Dittmar (2010). If a firm is acquired by

both a private equity firm and management, then the transactions are classified under multiple groups,

so there are a small number of overlaps in these subgroups. For 48 (301) firms (establishments), we

were unable to determine the acquirer type.

Table 1 Panels C and D provide summary statistics for the going private firm (target) as well as the

acquirer, respectively. Note that acquirer is the same as target when it is management that takes the

firm private. In both Panel C and D, the labor productivity of the plants (for both target and acquirer)

is larger than the industry average. Both total factor productivity (TFP) measures are systematically

higher than the industry mean for the acquirers (panel C), but not for target, as the Blundell-Bond

TFP measure is negative for all groups except the targets of private equity takeovers. Comparing

these panels, the going private sample has fewer plants in fewer industries with lower sales than the

acquirer. Further, the going private establishments have lower productivity than the acquirers, using

all three measures of productivity in t-1, with the exception of those that are unclassified.

before and after year -1. The number of firms in columns 5, 6 and 7 report the number of firm identifiers for the identified

establishments; because some going-private establishments in year -1 were bought from other firms before year -1 or sold to

other firms after year -1, the number of going-private firms is larger before and after year -1. For the control firms, attrition

in number of establishments more than offsets increases due to sales and so number of firms is the largest in year -1.

9

2.2 Key productivity measures

In this section, we discuss in detail the three alternative measures used in our analysis of establishment-

level productivity, as well as the variables and methodology used in their definition.

• Labor productivity: Labor productivity is defined as log real value of shipments divided by

employment. Value of shipments is simply the sales value deflated using 4-digit SIC industry-

specific output deflators (obtained from Becker and Gray’s (2009) NBER-CES manufacturing

industry database). Employment is the total number of employees reported in the ASM-CMF

database.

While labor productivity adjusts for increase in labor inputs, total factor productivity (TFP) mea-

sures adjust for changes in all inputs. A standard measure of TFP is the residual in a Cobb-Douglas

production function: yit = βmmit + βkkit + βeeit + βnnit + βllit + TFPit, where yit is the log of real

value of shipments of establishment i in year t, m is log real materials, k is log of real depreciated

capital stock, e is log of real depreciated energy costs, and n is log of white-collar (non-production)

employment and l is log of blue-collar (production) employment. The residual TFP is typically recov-

ered using a regression of real output on inputs; however this regression suffers from endogeneity as

inputs are likely to be chosen based on TFP (Marschak and Andrews 1944). We use two approaches

to address endogeneity concerns, as described below.7

• OLS-FE TFP measure: The main source of endogeneity in panel context is likely to arise

from fixed TFP heterogeneity across firms, arising from unobserved differences in factors such

as labor quality, locational advantages, and entrepreneurial quality. A solution is to use panel

data transformations with fixed effects, which control for any factors that do not change over

time. The OLS-FE productivity measure is defined as the residual from an OLS establishment-

fixed-effects regression of log real value of shipments on log real materials, log real energy costs,

log blue-collar employment, log white-collar employment and log real capital.

• Blundell-Bond system-GMM TFP measure: While the use of fixed effects to solve the

endogeneity problem is simple and appealing, in practice the approach often yields unrealistically

low (and sometimes even negative) coefficients for capital, because fixed effects increases the

noise component of the capital measure.8 Further, it is plausible that some productivity shocks

are anticipated, so that inputs respond to changes in productivity as well. An alternative solution

that addresses these issues is the “system” GMM approach, which uses both first differences

and levels (Blundell and Bond 2000), where lagged first-differences are used as instruments for

7In results available in an online appendix, we verified robustness to using three additional productivity measures: Solow

residual TFP measure, Levinsohn and Petrin TFP measure, and the Translog TFP measure. For brevity, we do not present

these in the paper but note the results are similar throughout.8Intuitively, capital often has lagged effects, and sometimes investment could have short-run disruptive effects. So changes

in capital often has low or negative correlation with contemporaneous changes in output.

10

equations in levels, in addition to lagged-levels as instruments for equations in first-differences.

A brief description of the Blundell-Bond (2000) procedure used by us is provided in the data

appendix.

Because the Blundell-Bond approach addresses potentially important endogeneity issues in the

most systematic manner, we use this measure as our main baseline measure. We also report the other

two measures because these are widely used in the productivity literature as well (and because the

Blundell-Bond coefficient estimates are sometimes sensitive to choice of instruments), so robustness

to these alternative measures would provide reassurance about the resilience of our results. When

comparing the magnitudes of labor productivity to TFP measures, it should be noted that if firms

experience larger increases in capital (or other inputs) relative to labor, the magnitude of the labor

productivity measure could be significantly higher than for TFP (because labor productivity does not

adjust for changes in capital).

For TFP measures, we allow production function coefficients to vary by 2-digit SIC code; as

expected (Griliches and Mairesse 1995) we find that relative to the Blundell-Bond estimator, the

coefficient on the employment variable is upward biased in simple OLS, while the capital coefficient is

downward biased when using OLS fixed effects.9

3 Analysis of productivity changes

When examining productivity and other outcomes, we present two sets of results. The first set of

“before-after” results summarizes what happened to the key variables of interest within the estab-

lishments that belonged to firms that went private, compared to their levels prior to going private.

The second set of “difference-in-differences” results presents the changes in the variables of interest

relative to changes in a matched control group of establishments. Both of these results use fairly

standard methodologies from the literature,which we discuss in the following sub-sections.

3.1 The before-after methodology

To examine before-after changes, we retain data for as many of the 13 years surrounding the event as

available for each establishment belonging to the firms that went private. These include up to 6 years

of data before the year of going private, the year of the firm went private, and up to 6 years after the

firm went private.10

9However, despite differences in coefficient estimates, we find that the estimated TFP residuals are very highly correlated

(correlation greater than 0.85) in our sample, so it is not surprising that in the analysis below we find consistent results across

alternative approaches. This consistency across alternative TFP estimators have been found by others using US Census micro

data e.g. Greenstone, Hornbeck and Moretti (2010).10Note that there may be some establishments that were born less than 6 years before the firm went private, and some

establishments that exit less than 6 years after the going-private event.

11

We then use simple regression approaches to summarize the before-after changes in two ways.

First, we use the following regression specification:

yit = βLR PRE LR PRE + βSR PRE SR PRE + βSR POST SR POST

+ βLR POST LR POST + fi + eit (1)

where yit stands for the dependent variable (productivity or other measures), fi stands for plant fixed

effects, and the four dummy variables are defined to capture four distinct time periods as follows: (i)

The long-run before going private: LR PRE is a dummy equal to one for the 3-year period from 6

to 4 years before going private and zero otherwise; (ii) The short-run before going private: SR PRE

is a dummy equal to one for the 3-year period from 3 to 1 years before going private and zero other-

wise; (iii) The short-run after going private: SR POST is a dummy equal to one for the 4-year period

from 0 to 3 years after going private and zero otherwise; and (iv) The long-run after going private:

LR POST is a dummy equal to one for the 3-year period from 4 to 6 years after going private and

zero otherwise. The term eit stands for residual error.

The estimates of interest are not the levels of the dependent variables, but rather their changes

over time.11 Specifically, we are interested in the following changes:

(i). Short-run post- versus short-run pre- going private (βSR POST − βSR PRE): This provides an

estimate of the short-run changes after going private, relative to the period just before the

going private event. Thus, if the new owners take steps that have immediate effects on the

performance of the plant, this should be reflected in this estimate.

(ii). Long-run post- versus short-run pre- going private (βLR POST − βSR PRE): This provides an

estimate of long-run changes in the dependent variable after, relative to the period just before,

the going private event. If the actions of the new owners take some time to have an impact, we

may obtain significant estimates here, but not in (i) above.

(iii). Test for prior trend (βSR PRE−βLR PRE): This provides an estimate of trends in the dependent

variable prior to going private. If the establishment was experiencing an increasing (decreasing)

trend in the dependent variable, this would manifest as a positive (negative) estimate in this

test. Thus, any changes we document in (i) or (ii) above, should be evaluated in the context of

the pre-existing trend captured by the estimate here.

The first three columns of Table 2 present the results of this analysis. Panel A includes all

establishments with available data. Panel B restricts the sample to only those establishments that

were not later sold in years t+1 to t+6 to ensure that the effects are due to going private and not

due to a subsequent sale (in either the going private or the control group). For all inferences, we

11The inclusion of the plant fixed effects implies that one of the time period dummies is not identified. However, our

estimation procedure reports the mean for the omitted LR PRE as the constant term.

12

compute p-values based on standard errors clustered by establishments.12 The first column regresses

labor productivity while columns 2 and 3 measure TFP (total factor productivity) according to the

two methods described briefly in section 2.2.

We find that there is a pre-existing improving trend in all the productivity variables (between

3.4% and 10.5%) prior to the going private decision, as evidenced by the positive and significant

coefficient on short-run pre versus long-run pre comparisons. Labor productivity and TFP increase

both in the short run (by 6.3% and about 3.2%, respectively) and the long run (by 7.2% and about

5% respectively) after going private and these differences are statistically significant. Because TFP

is measured as a residual from a regression of real output on inputs, increases in TFP translate to

increases in output holding inputs fixed; converting the three years means into annual rates, our

numbers indicate about a 1 percent annual increase in real output (net of increase in inputs) for the

going private establishments in post-going private period, but this is close to the per year increase in

the pre-going private period, so the regression results do not indicate a strong change in trend.

The first three columns of Table 2 Panel B present similar results. Again, in this panel, we exclude

establishments that are subsequently sold, resulting in approximately 8000 fewer establishment-year

observations. Similar to the full sample, the establishments exhibit a significant pre-period trend and

a significant increase in productivity from before to both the short and long run after going private.

In fact, the economic significance of the results are considerably stronger in this subsample, with

the productivity increase between 7 and 19 percent in the short run and 10 to 21 percent in the

long run. These results are consistent with the acquiring firm selling establishments that do not

experience an increase in productivity. In section 4.2.3, we will directly examine the characteristics of

the establishments that are sold.

In order to allow for a more flexible examination of the year-by-year effects (and for a direct visual

check for pre- and post-trends), we examine a standard event study graph, by plotting coefficients on

the index dummies from the following regression specification.

yit =

6∑k=−6

βkDk + fi + eit (2)

where k indexes the years after the going private event, and correspondingly Dk is a dummy variable

equal to one for the year k after the going private event. Negative values of k correspond to years

before going private. All other variables are as in (1) above. We then plot the βk coefficients as well

as the corresponding confidence intervals, to illustrate the trends in the dependent variable, and the

significance of the changes in the trends. Figure 1 shows that there is a statistically significant increase

in the productivity measures for the establishments after the firm goes private, for all alternative

measures. Further, consistent with the results in Table 2, there appears to be a strong pre-existing

increasing trend in almost all of the measures, though the figures suggest a short-run slowing down of

productivity before going private and a short-run acceleration after going private.

12The significance levels were similar when we clustered by firm.

13

3.2 The difference-in-differences methodology

The before-after analysis simply summarizes the trends in the variables of interest in the plants be-

longing to the firms that went private. However, these changes could be driven by factors specific to

the industry, or age-related changes (as plants are increasing in age over the period of our analysis).

Changes may also be driven by factors related to the pre-event size of the establishment, e.g. if

going-private firms’ establishments were relatively large, and if all large establishments experienced

relatively different patterns of productivity change.

In order to rigorously address potential bias from these industry, age and initial size related factors,

we form a matched control group for each establishment in the going-private sample.13 Specifically, for

each establishment in the going-private sample, we select up to eight matched control establishments

in the following way to allow a comparison to an average matched firms rather than a more limited

comparison sample. Using the data for the closest prior-to-going-private year in which the establish-

ment is observed in the ASM-CMF sample, we classify all establishments into 3-digit industry-age

quartile groups, using a two-way sort so that we have four groups for each. Then, we sort again

by employment within each industry-age quartile group; and we select up to four non-going-private

establishments just lower and up to four non-going-private establishments just greater in size than the

going-private establishment, for each going-private establishment. There are not always 8 matched

controls in cases where the going-private establishment was too close to the largest or smallest estab-

lishment within the industry-age quartile. For a very small sample (less than 3%) of establishments,

control groups overlap. We dropped all such control group establishments from our analysis, so that

control groups are unique to each going-private establishment.

This procedure generates non-overlapping ‘cells’, with one going-private establishment and up to

eight control establishments. We then estimate the following regression specification:

yijt = β0 + βLR PRE LR PRE + βSR PRE SR PRE + βSR POST SR POST

+ βLR POST LR POST +Djt + eijt (3)

where i refers to the plant, j refers to the cell that plant i belongs to, Djt refers to cell-year fixed

effect, and the other variables are as defined in the before-after specification (1) above. Note that

period dummy variables are defined only for the going private sample – for instance, LR PRE is a

dummy defined equal to one for going private establishments in the 3-year period from 6 to 4 years

before going private (zero otherwise). Thus, the intercept term (β0) captures the overall mean value

13The importance of industry variables (such as market structure, industry demand and capacity utilization) for firm

restructuring outcomes has been demonstrated in the literature, particularly by Kovenock and Phillips (1997) and Maksimovic

and Phillips (2008). The results in these papers suggest that it is crucial to compare outcomes holding industry-level shocks

constant, as we do using industry-age-size-year fixed effects in the baseline analysis, and other industry-specific year effects

in other analysis.

14

for the control group of establishments. The inclusion of the cell-year dummies (Djt) implies that the

coefficients on the period dummy are estimates of the differences between the going private establish-

ments and the control establishments, for that period.

Accordingly, the differences between the period dummies yield difference-in-differences estimates

that control for cell-year, or equivalently, industry-age-size-year effects (where size refers to the pre-

going-private size of the establishment). As before, we are interested in the three estimates detailed

in section 4.1, but defined as a difference from the control group, thus calculating a difference in

difference. This provides an estimate of the changes in differences between the going-private and

control group in each defined period relative to the period just before the going private event. If

both the going-private as well as their matched controls experienced similar changes in the dependent

variable, there would be no changes in the difference between the treated (i.e. going-private) and

the control group. The way the control groups are constructed, controls for any effects related to

industry-wide changes, or age-related changes or initial size related changes (or any combination of

these) are accounted for. In particular, industry-age-size specific year effects are controlled for in this

estimation. We also test if the difference between the going-private and the control firms is increasing

or decreasing in the pre-going private period. Though we match on age and size characteristics just

prior to the going private event, differences between the going private and control groups could exhibit

specific trends in the prior period. One particular concern would be that, relative to this matched

control group, the efficiency levels of the going-private establishments may have been on an up-trend;

that is, the going-private establishments may have been selected based on prior trends. Then, any

post-going-private changes may simply be a reflection of these relative trends. Therefore, this test

helps to establish whether differential prior trends are a source for biasing estimated difference-in-

differences changes.

Columns 4 to 6 in both Panels A and B of Table 2 present the results of this analysis. For all infer-

ences, we compute p-values based on standard errors clustered by the industry-size-age cells. As in the

first three columns, we regress labor productivity and two measures of TFP (total factor productivity)

according to the various methods described in section 2.2. We note three important results from this

table. First, we find that there is no pre-existing improving trend in any of the productivity variables

prior to the going private decision relative to the control establishments included in the regression.

This suggests that the earlier result of a trend in productivity for the private firms is also mimicked by

the control group of establishments, perhaps mirroring industry (or age or initial size) related trends.

Second, there is no evidence of a short-run increase in productivity variables for the going private es-

tablishments in a difference-in-differences sense when compared with the control group. This indicates

that while private firm establishments do have a short-run increase in productivity after exiting the

public markets, so do the control group establishments that do not change their public-private status.

Thus, we do not see any evidence of the pressures due to the short sighted behavior by the public

15

markets, or more severe agency problems for listed firms, which were postulated to be a drag on their

productivity. Third, we do not find any evidence of a long-run productivity increase in private firm

establishments, relative to the control sample. These results question the commonly held belief that

the U.S. stock market by its excessive focus on short-term earnings imposes myopic behavior on part

of the firm to meet such expectations.

Again, as in the case of the before-after analysis, we also examine a difference-in-differences event

study graph, by plotting coefficients on the index dummies from a DID version of equation 2 (with

cell-year fixed effects that control for industry-size-age-year effects), in Figure 2. Figure 2 confirms

the results in Table 3. First, the pre-going private trend is flat, confirming that the pre-going private

productivity trends are similar for going private group and the control group. Second, there is no

statistically significant short-run or long-run improvement in relative productivity for the going-private

establishments, compared to the pre-going private productivity levels.

Going private may still have had positive productivity consequences if it was the case that these

firms were headed for a relative decline in productivity. In other words, could it be that going private

enabled these establishments to match the performance of the control establishments, whereas without

that they would have performed relatively worse? We see no evidence for this possibility in Figures 1

and 2. First, Figure 1 shows strong improving trends in all productivity measures for the going-private

establishments; so the prior absolute productivity trends do not portend future distress. Second, none

of the productivity measures in Figure 2 show any significant dip prior to going private; in fact, the

trends are remarkably flat for most of the measures from years -2 to 0. Thus, there is no hint that

without going private, the going private establishments would have suffered declines in productivity.

3.3 Addressing endogeneity of the going private decision and related

selection-bias

One potential concern in our study is that the decision to go private is not random and thus it is im-

portant that we control for the endogeneity of the going private decision as it may impact productivity

changes. In particular, if the choice of firms to go private were based on some characteristics that

predict future improvements in productivity, then the before-after results in section 3.1 are biased by

this endogenous selection of going private firms. The difference-in-differences approach in section 3.2

controls for this issue, but only if the key drivers of future productivity changes are related to one (or

a combination of) industry, age, or plant size related factors.

We therefore employ two alternatives approaches to constructing the control group. First, we

utilize the results in Bharath and Dittmar (2010) to construct a propensity score matched control

sample. By matching on the propensity score, we test whether the establishments that went private

16

show an improvement over-and-above the improvement exhibited by firms that had a similar prob-

ability of being selected into going private treatment (Rosenbaum and Rubin 1985). Bharath and

Dittmar (2010) find that despite the fact that, on average, the private sample firms remain in the

public market for over thirteen years, firms that ultimately go private are very different and discernible

in information and liquidity considerations, relative to firms that remain public, throughout their public

life and even at the time of the IPO. They estimate a logit model using explanatory variables only

at the year following the IPO to predict if a firm will ultimately go private. They find that firms

that are more likely to ultimately go private have less analyst coverage, less institutional holdings,

more concentrated ownership, and more mutual fund ownership at the time of the IPO compared

to firms that remain public, supporting the importance of information considerations in the choice

between remaining public or private. They also find that firms that go private are more illiquid and

have less share turnover, supporting the importance of liquidity issues. Using a Receiver Operating

Characteristics (ROC) analysis, they show that the logit model has a 71% accuracy compared to a

bench mark of 50% accuracy with a random guess, reflecting substantial improvement over a naive

model. Bharath and Dittmar also show that the evolution of these firm characteristics impacts the

decision to go public. In so much as myopic behavior or agency problems impacts the changes in these

variables over time, myopia may influence the decision to go private; however, Bharath and Dittmar

do not directly examine myopia as a motivation for going private.

Motivated by these results, we construct a sample of the firms with the closest propensity to the

going private score of each of the firms in the going private sample from a pool of firms that did

not go private. We then use their establishments as the matched sample controls. In calculating the

propensity to go private, we use the firm specific control variables at the time of the IPO as in Bharath

and Dittmar (2010). Specifically, we use log sales, analyst coverage, R&D, capital expenditures, a

dividend dummy, turnover, market to book, free cash flow, leverage, cash, net fixed assets, and the

number of past mergers to estimate the probability a firm will go private, as in column 2 of Table 7

of Bharath and Dittmar (2010) to estimate the propensity to go private. Since these control variables

are not available for all firms in our sample, the number of establishments of firms that went private

drops from 28,518 in Table 2 to 22,488 in these estimations.14 Similar to the approach in section

3.2, we include industry-propensity cell-year fixed effects in each regression. Here a ‘cell’ refers to the

unique (i.e., non-overlapping) group of establishments comprising one going private establishment and

the control group of establishments matched (based on propensity score and industry) to this going

private establishment. Then for each of the cells, we include cell-year fixed effects in the regressions.

Accordingly, as in section 3.2, the estimated effects are the mean of the relative difference between

14One noteworthy attribute of this control group (and another reason for the drop in the number of observations) is that

all of the firms in the control group are also listed firms, as the propensity model in Bharath and Dittmar (2010) uses stock

market related variables. In section 3.2, the control group was establishments of all non-going private firms, which include

both listed and unlisted firms.

17

each going-private establishment and its matched control group. For all inferences we compute p-

values based on standard errors clustered by industry-propensity score cells in the table.

In addition to using the propensity match, we also employ a third matching criteria to address the

potential bias from selection on past productivity. Kovenock and Phillips (1997) show that productivity

is (negatively) related to restructuring decisions such as plant closures. In our context, if relatively more

productive firms went private, then there could be less scope for improvements for these plants relative

to matched plants that may have lower initial productivity. While the lack of differential prior trends

in our baseline DID analysis assuages concerns on this count to some extent, one direct approach

to addressing this concern is to include ex-ante productivity as one of the matching variables.15

Specifically, instead of establishment size used in our baseline DID approach, we use the Blundell-

Bond TFP measure, and match each going private plant to up to eight establishments closest in this

productivity measure, from within the same 3 digit industry and age quartile.

The results from the difference in difference analysis using the two alternative approaches are

presented in Table 3 Panels A and B. Similar to Table 2, Panel A presents the full sample and Panel B

presents the subset of firms that are not subsequently sold. The first three columns in both panels show

that relative to firms with a similar propensity to go private, the sample firms exhibit no pre-existing

trend in productivity. More importantly, consistent with the results in Table 2, the firms exhibit no

significant improvement in either the short-run or long-run period after going private. Columns 4

through 6 of both panels of Table 3 present the results from difference in difference estimation with

the sample matched by past TFP. Here there is evidence of a pre-existing relative trend, with two of

the three measures showing an increase in productivity from the long run period prior to going private

to the short run period prior to going private. Thus, relative to other establishments with a similar

TFP level in the year before going private, the going private establishments’ productivity is on an

upward trajectory. However, similar to the results using the other control groups, the going private

firms do not show significant relative (DID) improvement in productivity between the pre and post

periods. Specifically, in all specifications, there is not significant DID increases in productivity, in both

the short- and long-run. On the OLS TFP measure, establishments have a statistically significant

decrease in productivity after going private relative to the control group, but the economic magnitude

is small: -2.7% over the six year long-run period, translating to less than -0.5% per year. Overall, this

evidence confirms that though firms that go private improve their productivity in absolute terms, this

improvement is not better than other similar firms.

15We thank one of the referees for suggesting this method.

18

4 Analysis of other outcomes

The previous results establish the fact that while labor and TFP productivity improves for the private

firm establishments, it does not improve differentially compared to the public firm establishments.

By implication, this analysis strongly suggests that there is no evidence for either ‘over’ or ‘under’

investment in listed firms, as sub-optimal investments should result in a negative effect on operational

efficiency, and hence lead to improvements in productivity post-going private.

Nevertheless, it is interesting and informative to examine investment directly and thus in this sec-

tion we examine the change in investment by studying two other sets of outcomes. First, we look

at capital and employment changes in establishments belonging to going-private firms. If the capital

market’s short-term outlook forces listed firms to sacrifice long term growth by reducing investment

and limiting expansions, we should expect to see greater investment and corresponding employment

expansion at the establishment level after firm’s go private.

Second, we examine a firm’s propensity to sell or close plants. Again, if market short-termism

causes firms to be impatient with short-term poor performance of some new projects or establish-

ments, we would expect to see a greater nurturing of investment in plants after going private, and

correspondingly we could expect to see a lower probability of selling off or shutting down plants after

going private.

4.1 Analysis of capital and employment

In this section, we examine the capital and employment choices around the going private decision. The

regression specifications are identical to that in section 3, except for the change in dependent variables.

Table 4 presents the regression results and Figure 3 summarizes the coefficients in the regression with

the confidence intervals. We present four specifications: the before and after, the difference in differ-

ence matching as we did in Table 2, difference in difference matching as we did in Table 3 including

past TFP, and difference in difference as we did in Table 3 matching by the propensity to go private.

We find that while log deflated capital for the private establishments increases by 2.5% (specification

1a) in the short run before-after comparison, they actually decline by 5 to 19% relative to the control

groups over this same period. Also, while in absolute terms capital increases by 6.4% in the long-run

after going private, again relative to the alternative control groups they show a large and significant

decline of about 15 to 36%. We also find that there was a statistically significant upward trend in

capital in the going private establishments in absolute terms (1.7% in Column 1a) and relative to the

control sample matched by ex ante-TFP, but there was no statistically significant prior trend relative

to the other control groups. Log employment shows a decline in absolute terms both in the short run

(5.8%) and the long term (8.9%) in specification 2a. This seems in line with a prior trend decline of

3.9%. This pattern of declines in employment is also seen relative to two of the three control groups

19

in specifications 1b and 1c. In specification 2d (propensity matched sample), while there is a decline,

the change is not significantly different from zero.

In summary, we find that establishment productivity demonstrates no significant difference after

going private relative to several control samples. And, establishments decrease investment in cap-

ital and employment after going private. Taken together, these results provide no support for an

acceleration of investment after going private; thus, public markets do not lead to under-investment.

If anything, the DID results (relative to other listed, propensity score matched firms) suggest that

a positive relative trend in capital and employment is reversed in a significant way after the going

private event.16 These results suggest that public markets lead firms to invest more rather than less

compared to private firms. Thus, these results contradict the commonly held view that market myopia

leads to under-investment. Taken alone, the results for capital and employment could suggest over-

investment, either because of myopia (such as in Bebchuk and Stole (1993), Bizjak et al (1993)), or

agency problems leading to empire-building in the going private firms when they were listed; however,

this interpretation is not consistent with difference-in-differences results showing that firms produc-

tivity does not change after going private, detailed in the previous section. In other words if firms

had been overinvesting when they were public, we would have expected to see an improvement in

productivity but we find no evidence of such a change relative to control groups.

4.2 Analysis of plant exit (shut-down and sales) decisions

In this section, we examine if firms are more nurturing of or patient with establishments after going

private. Because all of our analysis is based on examining going-private establishments that were

operational in the year before going private, the analysis here will essentially examine the exit rate for

gone-private establishments, relative to the industry, size, age control group. In other words, we will

be looking at differenced means, and we will not be doing a before-after or difference-in-differences

analysis, as essentially the sample conditions on no exit in the pre-going private period (so that there

is no “before” period, and hence no “difference-in-differences” analysis possible.) In this analysis, we

analyze all exits as well as separately examine sales and closings of plants. Because the exit data

derives from the Longitudinal Business database, it is not limited to manufacturing firms. Thus, in

our initial analysis of exits, we include a much larger sample than that used in the earlier tables.

However, in our primary analysis of exit rates, we will control for productivity which is only available

for manufacturing establishments, thus again limiting our sample the manufacturing firms.

16In the context of the productivity results, one relevant question is why the relative downsizing on the input side in capital

and employment did not translate into productivity gains; in untabulated results we find that sales declined in line with the

decreases in capital and employment, so that the input declines were not TFP enhancing.

20

4.2.1 Exit summary statistics

We begin with summary statistics of exit rates by industry (analogous to Table 2 in Kovenock and

Phillips 1997), various industry classifications (used in Maksimovic and Phillips 2008), time periods,

and acquirer type. This analysis is similar to presented in Maksimovic, Phillips and Prabhala (2011),

allowing us to compare our summary statistics for the going private transactions to their much broader

analysis of mergers and full-firm acquisitions. Table 5 Panel A presents the percent of establishments

in the going private sample that are sold or closed in the 3 to 6 years following the going private

transaction. As in Kovenock and Phillips (1997), the statistics show wide variance in exit rates (as

well as sale and closure rates) across industries: within these 16 industries the exit rates range from

27 to 58% over the first three years and 50 to 69% over six years, with the paper industry having the

highest exit rate and apparel and textiles having the lowest.

Panel B illustrates how the exit rates vary over time and by type of acquirer and industry classi-

fications. In addition to this, we provide summary statistics from Maksimovic, Phillips and Prabhala

(2011) labeled “MPP” for comparison. The top section of Table 5 panel B shows that the going

private firms exit plants (both through sale and closure) at a higher rate than the control sample over

the 3 and 6 year horizon. This pattern relative to controls is the same as in MPP as they find greater

exit and closure for target firm establishments as well. The exit rate in our sample over the three year

horizon (36.5%) is lower than that in the much broader MPP M&A sample; interestingly the closure

rate is higher in our sample (27.9% vs 18.6%), while the sale rate is much lower (8.6% vs 27.0%).

Thus, it appears that going private firm establishments exit strategy is more reliant on closure than

for the typical M&A target establishments. The patterns (and hence comparison to MPP) are roughly

consistent across acquirer types (in top row) and industry classifications (in bottom row). In the mid-

dle row, the split by the 1990s and 1980s show greater exit (both by sale and closure) in the 1990s,

which is consistent with the relative patterns found by MPP. In the bottom row, not surprisingly the

largest amount of exits (both sales and closures) are found in declining industries, both for the going

private sample and the control firms.17

4.2.2 Exit hazard and propensity analysis

To examine how changes in establishment characteristics and the private firm status impact the

probability of establishment exits, we first use a hazard model to investigate if and when a plant is

closed down or sold. Specifically, we are interested in the length of time it takes for a plant to be shut

17One difference between our sample statistics and MPP is that the exit rates for our control group is much closer to the

going private target sample than is the case for MPP, where the control group has starkly smaller exit rates. We speculate

that this could be because we use a more specific industry-size-age matched control group, while MPP look at all other firms

within the industry. Exit patterns are likely to be more similar once we condition on age and size – as is well documented in

the literature, and the hazard models in Table 6A confirm, that plant exit is strongly correlated with plant size and age.

21

down or sold from the date of going private, and the influence of different variables on that duration,

controlling for the fact that our comparison plants may also have exit decisions at some unobservable

time. In the baseline case, we use the exponential proportional hazard model. The model to be

estimated is:

h(t,X) = h(t, 0)exp(β′X)) = exp(β′X)) (4)

where h(t,X) is the hazard rate at time t for a firm with covariates X. A positive coefficient on variable

x in the hazard model implies that a higher x is linked to higher hazard rate and thus a lower expected

duration. The hazard ratio which is simply exp(β) tells us how much the hazard (i.e., instantaneous

risk) of exit increases for a unit change in the independent variable.18

In each estimation, the sample includes going private establishments and matched controls. As

explained in section 3.2 and 3.3, each going private establishment is matched to a unique set control

establishments (depending on data availability); each of the matched controls are assigned the same

“start/birth” year as the matched going private establishment. We use time invariant characteristics

(defined as at the year before going private) to explain duration, with the main variable of interest

being the going-private dummy variable. We include establishment log employment, age, 2-digit in-

dustry and year fixed effects as controls.19 That is, our goal is to understand whether the going private

establishments have a higher hazard for exit relative to the control group, controlling for size, age,

industry, and common year shocks.

Here, each plant we examine at the year before going private has two competing event outcomes

– sale or closure. We record only the earlier of the two outcomes; thus if a plant is sold, then it is no

longer at risk of being closed (as we are not interested in decisions made by the new owner), and vice

versa (i.e., after a plant is closed there is no possibility of it being sold). In the baseline analysis of sale

(closure) in Columns 2 and 3 of Panel A of Table 6A, exit into closure (sale) is treated as censoring. As

textbook discussions of competing risks (e.g. Pintilie 2006) point out, treating the competing event

as censoring can be misleading, and ideally we need a framework that adequately counts subjects that

experience the competing event as not having any chance of experiencing alternative event. We use

the competing risk model proposed by Fine and Gray (1999), which allows for assessing the effect of

covariates on the subhazard for each of the individual event.20

The results of this analysis are presented in Table 6, Panel A. The first three columns present the

18In untabulated results, we checked and found our results to be very similar when using the Cox proportional hazard model,

where the baseline hazard h(t, 0) is allowed to be arbitrary (instead of set to 1 in the exponential model); the Cox partial

likelihood estimator provides a way of estimating β without estimating h(t,0).19To clarify, for each establishment, there is only one observation, with the age and log employment defined as of the

year before going private. (For controls, the relevant year is the year before going private for the going private firm they are

matched to.)20This is implemented using the stata command stccreg. We thank the editor for suggesting this approach.

22

hazard rate of exiting, selling, or closing, respectively. Columns 4a and 4b present the subhazards from

the competing risk model. Compared to the industry-age-size matched control group, we find that

going private firms have statistically higher hazard rate of both closing and selling a plant. Specifically,

these establishments have a 28 (8)% higher hazard rate of selling (closing) a plant which translates

into a hazard rate of 1.3 (1.06). Using the competing risk model, going private establishments have

a close to 30% significantly higher hazard rate of closing or being sold; the competing risk analysis

suggests that the lower hazard for closure is downward biased by treating sales events as equivalent

to censoring. Overall these hazard rate results show no evidence of a lower exit rate for going private

firms’ plants and after controlling for the propensity to go private the change is not significant.

The main strength of the hazard rate model is that it explicitly accounts for the sizeable right

censoring that occurs in analysis such as ours (i.e., a sizeable number of establishments survive and

are neither sold nor closed till the last year of the dataset). However, we recognize that it is not

computationally feasible to use cell-year fixed effects in a hazard model, similar to how we did in the

previous section. Thus, in order to check robustness to using higher dimensional fixed effects, we

repeat this analysis using a linear propensity model in Panel B.21 To do this, we first define two exit

variables: 3- (6-) year exit dummy is a variable that equals one if the plant exited (was either sold or

closed) in the three (six) years after the going private event and zero otherwise. Similarly we define

two sale and closure dummy variables. The results are presented in Table 6 Panel B. All columns

include industry-size-age-year effects, so that the going private effect is evaluated relative to matched

control establishments in the same cell. We find that establishments of the going-private firms have

a 3.1% (5.1%) higher propensity than the industry-age-size matched control group to shut down a

plant with in the next 3 (6) years, immediately after going private. As in the hazard analysis, the

same patterns hold for both sale and closure: sale (closure) rate is 1.5% (1.5%) higher over the 3 year

window, and 4.3% (0.8%) higher over the six year window, with all estimates significant at the 1% level.

The analysis in Panel A and Panel B control for fixed and time varying industry effects, which

subsumes the effect of specific industry characteristics. In order to understand the impact of specific

industry characteristics and relate to previous literature that examined closures and sales, in Panels C

and D of Table 6, we repeat the propensity analysis including only year fixed effects, and covariates

for ex-ante (i.e., in year prior to going private) productivity, employment, number of plants, industry

capital utilization, industry concentration, change in industry aggregate output demand, and industry

classifications. Capacity utilization measure is taken from Gorodnichenko and Shapiro (2011); they

use the US Census Bureau’s survey of capacity utilization to construct industry specific capacity

utilization measures. Industry concentration is measured as in Kovenock and Phillips (1997) (KP1997)

21This approach has the drawback that it does not address the right censoring issue; however, our checks dropping plants

that survive to the end of the data period yielded very similar results to those reported in Panel B, so we are confident that

the qualitative conclusions are not biased by the right-censoring of the data.

23

as the market share of the top four plants in the industry. Change in output demand is defined as

aggregate change in industry output. Industry classifications used in Panel D are based on definitions

in Maksimovic and Phillips (2008) (MP2008).22 KP and MP in their work find these factors to be

important determinants plant exits.

Because we control for variables only available for manufacturing firms, the results in Panel C and

D also provide evidence of the robustness of the exit analysis for this subsample. We find that in

each specification and across both the three and six year horizons going private establishments have a

significantly higher rate of being sold or closed. Specifically, going private establishments have between

a 4.7 to 7.1% higher probability of being sold and a 3.9 to 5.3% higher probability of being closed than

the matched sample. Consistent with the findings in Table 3 of KP1997, plant closure (Column 3 in

panel C and D) is generally negatively correlated with plant productivity, size of firm (measured as firm

employment), and change in output demand. Interestingly, and consistent again with KP1997, number

of plants owned by the firm is positively correlated with closure; thus conditional on firm employment,

a plant belonging to a firm with more plants is more likely to be closed. As in KP1997, we find

negative effects of capacity utilization and lagged industry concentration (as in KP1997) but these are

weaker (not statistically significant). The pattern is similar for sales (Column 2) with respect to firm

employment and number of plants owned by the firm, but labor productivity is not strongly correlated

with sale propensity (so more or less productive plants are not systematically targeted for sale), and

change in industry demand and lagged industry concentration are weakly positively correlated with

sales over both the three and six year horizons. In panel D, we find the strongest closure rates for

declining industries, and the largest sales rates for consolidating industries, which is consistent with

what could be expected from the definitions of these industries in MP2008.

The results from the hazard and propensity models suggest that the going private firms accelerate

plant sales and shut downs after going private. Again, this evidence does not support a myopia-related

hypothesis that the elimination of stock market’s short-term focus make it more likely for firms to

nurture plants after going private.

4.2.3 Selection of plants for closure

In this subsection, we examine if the going private firms differentially target the poorly performing

plants (in a labor productivity and TFP sense) for closure. There are two motivations for doing this

analysis. First, this analysis could help understand the motivations for going private transactions, given

22Specifically, as discussed in MP (pp674): (1) Growth industries In Growth industries long-run industry shipments and

the long-run (which we define over the period 1981 to 2001 number of firms are increasing, and changes for each of these

factors are above the median industry change. (2) Consolidating industries In Consolidating industries the change in long-run

shipments is above the median industry change but the change in the number of firms is below the median. (3) Technological

Change industriesIn Technological Change industries, the change in long-run demand is below the median industry change

but the change in the number of firms is above the median. (4) Declining industriesIn Declining industries, the change in

long-run demand and the change in long-run number of firms are both below the median industry change.

24

that our earlier results show no gains in productivity at the establishment-level. If firms differentially

target lower productivity establishments for closure, this could help them to retain higher productive

plants in their portfolio.23 Second, arguably if the stock markets are indeed myopic, we would expect

to see market pressures leading to stronger targeting of worse performing plants by public firms (which

may need to be nurtured to achieve greater productivity levels). Thus, we predict a less negative effect

of productivity on shutdown decision after the firm goes private, as evidence of market myopia.

To test for differential targeting, we add the productivity measures as well as an interaction term

between the going private dummy and productivity, to the Hazard and OLS model specification in

Table 6 (panels A and B). Results are in Table 7. The control group is composed of establishments

matched on industry, age and initial size (as explained in section 3.2). The hazard model in Panel A

of Table 7 includes controls for employment and age (not reported for brevity), and industry and year

fixed effects, while the OLS model panel B includes much more detailed industry-size-age cell-year

fixed effects.

Across all the specifications, the coefficient estimates on the interaction term are always negative

when we look at the summary exit variable (Column 1) and sales decisions (Column 2). The same

is the case for closure with one exception (OLS FE TFP in Panel A). The effects are statistically

significant about half the cases, with magnitudes on the interaction term for exit in Panel B for labor

productivity (Blundell-Bond TFP) suggesting that plants with a 10% lower than mean productivity

would have a 0.4% (1%) higher propensity to be exited (closed or sold). The magnitudes suggest a

stronger targeting for sales in Panel B (as magnitudes for cross-terms for sales are higher), but this is

true only for the Blundell-Bond TFP measure in Panel B. Interestingly, across all the measures, the

average effect of productivity for sales (Column 2) is positive (and significant) in Panel A and close to

zero on Panel B, so that low productivity establishments are not targeted for sale on average, but the

negative interaction term shows that going private firms more aggressively target lower productivity

plants for sale, consistent with trying to improve the productivity of the portfolio of remaining plants,

and inconsistent with myopia.

Column 3 examines closure decisions, and two points are noteworthy here. First, we find that

the coefficient on productivity measures is always negative, consistent with Kovenock and Phillips

1997, and results in Table 6. This suggests that productivity (as measured here) is indeed informative

and guides the plant shut down decisions of all firms. Second, and more importantly for our study,

the coefficient on the interaction term of the productivity variables with the going private dummy

is generally negative (though not statistically significant in most cases). This suggests some weak

evidence that going private firms also more aggressively target less productive establishments for

23Also, while we do not have the data to verify this, firms may be selling the plants or assets of closed plants above the

pricing implicit in the going private transaction to gain value. Consistent with this, in untabulated results we find some

evidence that going private firms targeted plants in higher income areas for closure (presumably land and building in these

areas would be the most valuable).

25

closure, again consistent with attempting to improve productivity of their remaining portfolio and

contradicting the myopia story.

Taken together, these findings indicate that public firms do not seem to be unduly affected by

capital market pressures to be less patient with poorly performing plants; if anything, after going

private firms appear quicker to close and sell poorly performing plants.

4.3 Results by acquirer type

There are multiple ways a firm can go private, and our sample includes transactions that are driven by

private equity firms, management, and private operating firms. In the main analysis, we treat these

deals uniformly. However, it is possible that the productivity dynamics, as well as capital/investment

and shutdown choices differ by the parties involved in the transaction. We classify the sample firms

that went private into three categories: a buyout by a private operating firm, a buy out by a private

equity firm, a buy out by the management. We source the classifications for these deals using news

paper reports from Factiva. Specifically, we read news articles regarding each deal and classify each

into categories based on parties involved in the transactions. When it is unclear, we classify the deals

as unclassified. The category-types are non-exclusive, so that some deals may involve deals classified

under more than one type.

As discussed in the introduction, this analysis helps evaluate two alternative explanations for the

results documented thus far. One potential explanation is that going private transactions are highly

leveraged, and this leads to short term pressures even after going private, which in turn prevents

the firm from adopting long-term strategies to improve productivity, and also pressures the firm to

downsize and close/sell plants. Because leverage is more likely to be higher for private equity and

management buy-outs, the operating firm takeover subsample will allow us to examine results less

likely to be confounded by effects of high leverage. Second, independent of the extent of leverage,

investors driving going private transactions may have short horizons. Again, this is more likely to hold

for private equity transactions, than for operating firm buyouts (and arguably managers in MBOs also

have long-term horizons).

Table 1 Panel B shows that there are 819 establishments associated with a management buyout,

1,207 with a operating firm and 958 with a private equity acquirer, similar to the relative breakdown in

Bharath and Dittmar (2010). Thus 45% of our sample consists of operating firm acquisitions, which

are less likely to be affected by both leverage effects and private equity myopia effects.24

24To confirm the assumption that operating firm acquisitions do not have significant increases in leverage, we manually

collected leverage data for a random sub-sample of 30 such events for the year 1999 from the Capital IQ database. For

comparison, Guo, Hotchkiss and Song (2011) have 21 buyout firms in 1999 with post buy out data in their sample, which

26

In Table 8 Panel A, we present comparisons of analysis of productivity and TFP that is similar to

Table 2, and Panel B presents analysis of capital and labor similar to Table 4. Both are relative to

a control group composed of establishments matched on industry, age and initial size (as explained

in Section 3.2). The same analysis using establishments within 3-digit industry propensity (to go

private) score matched control group (described in Section 3.3), yield qualitatively similar results but

are omitted here for brevity (and are available on request from the authors).

Table 2 shows that on average going private establishments did not improve based on any measure

of productivity relative to a sample matched by Industry, size, age and year. In general, Table 8 Panel

A confirms this finding across each acquirer type. Specifically, no acquirer type shows any significant

difference in labor productivity or TFP using the OLS methods. There is weak evidence that unclassi-

fied and management-led buyouts had a decrease in the short run period after going private. But, this

result is inconsistent across different productivity measures. The only positive and significant change

in productivity are for those taken private by an operating firm using the Blundell Bond TFP measure

and only in the longer run. Again, the result is not consistent across TFP measures.25 Overall, the

evidence in the subgroup analysis is consistent with the full sample for each of the subgroups. Most

importantly, there is no evidence that operating firm takeovers, which are least susceptible to pressures

from high debt or impatient investors, show any improvements in productivity.

Table 4 showed evidence of a decrease in both labor and capital investment for the full sample. In

Table 8 Panel B, we find similar evidence of a decrease in capital investment across all acquirer types.

Thus, the decrease in investment is not driven by one subgroup, and again operating firm takeovers

show significant capital decline as well. The decrease in employment is consistent in sign, but not

significant for operating firms, and interestingly also not significant in the short-run for private equity

firms.

Table 8 panel C shows that management acquirers are in fact less likely to exit, which is driven

by their much lower propensity to close plants, though they do have a higher than control group

propensity to sell plants. Operating and private equity acquirers show a greater propensity to both

sell and close plants compared to their control groups. These results are consistent across the hazard

analysis in Panel C-A and the linear probability analysis in C-B. (One small exception is that private

equity firms seem less likely to close over the long term window (Column 3 of 6 year outcome dummy

they used to conclude that leverage increases dramatically after buy out events. We then examined average Long Term Debt

to Equity for the period before and after the acquisitions. We found that mean (median) leverage ratios for this sub-sample

actually drops by -5%(-1.25%). This confirms that operating firm acquisitions were not accompanied by large increases in

debt, so effects found for this sample of operating firm acquisitions are unlikely to be influenced by effects of excess leverage.25Further in Supplementary Appendix Table A.2, we find that this positive result is not robust to matching on ex-ante TFP.

27

in Panel C-B), which may be because they are most aggressive about exiting through sales.)

Finally, in Table 8, Panel D we replicate the OLS analysis in panel B of Table 7, examining whether

particular types of acquirers were more aggressive in targeting lower performing plants for exit (we got

similar results using the hazard model of panel A, Table 7). We find that the operating acquirers more

aggressively targeted low labor productivity plants for both sales and closures; the results for sales are

significant with the other two TFP measures as well, but the closure results are small and insignificant

(and positive with Blundell-Bond TFP). The interaction term is also systematically negative (though

not always statistically significant) for private equity acquirers, suggesting weak evidence that these

acquirers were also more aggressive in exiting low productivity plants. Interestingly, the estimates are

smaller and always insignificant for management acquirers; they do not seem to be more aggressive in

selling or closing low productivity plants. The results suggest that both operating and private equity

acquirers may be partly motivated by potential gains (say from high selling prices) through more

aggressive exit of poorly performing plants, while management does not seem to use this strategy.

Across Panel C and D, there is some evidence that Private equity acquirers are aggressive about

exiting (either through sale or closure), which could be consistent with “private equity myopia”;

however, the magnitudes of effects are similarly large for operating acquirers as well, so private equity

myopia is not the sole explanation for more aggressive exit found in the full sample.

5 Other robustness checks and discussion of results

In this section, we discuss a number of robustness checks and other tests that help to validate and

explain the conclusions from the baseline analyses. Results are available on request from the authors,

unless otherwise stated that they are included in a supplementary appendix available online.

(i) Examining outcomes at the acquirer firm: The above analyses document that there was no

change in productivity, an increased probability of sales or shutdown, and relative declines in employ-

ment and capital, at the going private establishments, relative to control groups. We interpret this as

evidence against capital market myopia or that myopia in the capital market is not worse than that

in the private market, as myopia would be expected to induce under- (or over-) investment relative

to what is optimal for productivity. It is possible that outcomes at the acquiring entity may be dif-

ferent in a way that affects this interpretation. To investigate this possibility, we use firm ownership

identification data in the Census LBD dataset to identify the acquirer firm (and its establishments at

the time of the going private event).26 We investigate three sets of outcomes. First, it could be the

26Firm ownership identifiers are not updated every year in the LBD, as documented by Jarmin and Miranda (2002).

Accordingly, all of the owner firm identifiers do not switch to that of the acquirer firm in the year after the going private

event. To overcome this limitation within the constraints of the data, for each going private establishment we track the firm

identifier and identify the first time it changes after the going private event. (Ideally, the firm identifier would change in the

year of the going private event; we allow for the possibility of delayed reporting by tracking later years and identifying the

28

case that the acquirer firm expands operations in its establishments, offsetting the declines in inputs

documented in the going private establishments. We analyzed changes in capital and employment

(and sales) at the acquirer firm establishments following the same approach as used for analyzing the

going private establishments (using a control group matched on industry, age and size at time of the

going private event). Contrary to what would be expected if there was a countervailing expansion

at acquirer firm plants, we found no difference-in-differences increases in capital or employment (or

sales) at the acquirer firm plants (both overall, as well as in a sample restricted to be in the industry

of the target going private firm) – in fact in most cases we found significant DID declines in inputs.

Second, it could be the case that the acquiring entity opens new establishments in the same industry

as the plants shutdown at the target going private firm. We analyzed the propensity to open a new

establishment and found that this propensity declines significantly in absolute terms at the acquirer

entities, and shows no differential change relative to a control group. Third, we checked whether

total factor productivity went up at acquirer establishment (say from a transfer of clients or markets

that the acquirer’s plants were able to serve without increasing inputs proportionately). We found

no significant DID changes in any of the three baseline productivity measures at the acquirer firms’

establishments, either in the short-term or the long-term. Thus, we conclude that there is limited

evidence for changes at the acquirer firm establishments offsetting (or complicating interpretation of)

changes documented at the going-private establishments.

(ii) Using operating profit measures as an alternative to productivity: As a further check to rule

out noise in productivity measures as an explanation for the baseline results, we examine two operating

profit measures: (i) gross operating profits, defined as sales less sum of materials cost, energy costs,

blue collar wage bill and white collar wage bill; and (ii) the ratio of gross operating profits to sales. We

find results (presented in Supplementary Appendix A.3) that are very similar to those in the baseline

productivity analysis; neither profitability measures show significant improvement after establishments

go private, relative to the two control groups. We also examine the ratio of different cost components

(materials, energy, blue collar wage bill and white collar wage bill) to sales, and find no significant

DID changes in any of these components.

(iii) Splitting the sample over time: It is possible that the responsiveness of managers to the stock

market’s short-term focus may have been exacerbated by the increasing use of stock options in exec-

utive compensation. Similarly, if leverage changes were driving any of our results, the use of leverage

was much higher in private equity deals in the 1980s than in 1990 and beyond. To test whether the

productivity and investment responses to going private have changed over time, we repeat the analysis

in Tables 2, 3, 4 and 5 separately for the going private transactions that occurred before and after

1992. In untabulated results, we find no notable differences between these samples; the qualitative

first change.) We then define as the acquirer firm the modal new firm identifier across all the acquired plants within a going

private firm.

29

conclusions of the analysis are the same for these samples separately as it was for the overall sample.

(iv) Relation to findings in Bharath and Dittmar (2010): Bharath and Dittmar examine why

firms go private, tracking firms from their IPO to the time they go private. They employ both a

hazard model using the full panel of data as well as a logit model using explanatory variables only

at the year following the IPO to predict if and when a firm will ultimately go private. As mentioned

earlier, they find that despite the fact that, on average, the private sample firms remain in the public

market for over thirteen years, firms that ultimately go private are very different and discernible in

information and liquidity considerations, relative to firms that remain public, throughout their public

life and even at the time of the IPO. However, the changes in the characteristics over time are also

important. Bharath and Dittmar show this by implementing a hazard model utilizing a control sample

that is matched by firm characteristics at the time of the IPO. They find that firms that are more

likely to ultimately go private have less analyst coverage, less institutional holdings, more concentrated

ownership, and more mutual fund ownership both at the time of the IPO and that these characteristics

change over time to increase the hazard rate of going private. Further, in the hazard model compared

to the matched sample, firms have a higher hazard rate of going private when they have a lower

market to book ratio. Though Bharath and Dittmar do not directly test for the importance of myopia

in the decision to go private, their results that the decline in market to book, turnover, and other

measures leave open the possibility that capital market myopia or agency conflicts could impact the

decision to go private. In this paper, we extend their findings to directly test for the impact of capi-

tal market myopia and agency problems on firms when they are private relative to when they are public.

(v) Other checks: We perform a number of other checks of the baseline results. These include (a)

checking our results to using plant fixed effects and industry-year effects (instead of cell-year) effects

in our DID specifications; (b) checking the split-by-acquirer-type results in Table 8 (Panel B and C)

using the event time figures; (c) redoing the baseline results adjusting for sampling weights (the base-

line analysis treats the ASM-CMF sample as an unbalanced panel) to check if sampling systematically

affects the going private sample differentially relative to the control groups. We find our results robust

to these checks.

6 Conclusion

An important critique of the stock market oriented U.S. financial system is that its excessive focus

on short term quarterly earnings forces public firms to behave in a myopic manner and that agency

conflicts due to disperse ownership leads managers to invest in ways that are inconsistent with share-

holders’ interests . We hypothesize that if U.S. firms are myopic in a manner that affects operational

efficiency and agency conflict impacts investing and operating decisions, then instances of going private

30

(when these constraints are eliminated) should cause U.S. firms to improve their establishment level

productivity (by focusing on long-term decisions) relative to peers. We find no evidence that this is the

case. Our key finding is that while there is evidence for substantial within establishment increases in

productivity (about 3% to 6%) after going private, there is little evidence of difference-in-differences

efficiency gains relative to a peer group of establishments constructed to control for industry, age,

initial size (at the time of going private) and the endogeneity of the going private decision effects. In

further evidence that is contrary to the standard myopia story, we find that going private firms contract

(decrease capital and employment), and exit (sell and close) plants more quickly than peer groups.

Our findings cast doubt on the view that stock markets force publicly listed firms to be short-sighted.

Our results provides an interesting contrast to Kaplan (1989) and Long and Ravenscraft (1993),

who find improvements in investment and accounting profits after a LBO. Similar to their studies, and

in our before and after analysis, we find that there is an increase in productivity and capital (though

not employment) after going private. The contribution of our study is that by employing census data

and a careful matching procedure, we are able to show that going private did not likely cause this

increase in productivity as the change is not significant once compared to an ex-ante similar sample.

However, the data, sample and comparison firms in both of these papers differ from that employed

here, so comparisons between our results and theirs must be done with caution. With this caveat

in mind, our findings suggest that using matching techniques to control for the industry, size, age,

productivity as well as the propensity to go private (using several variables shown to predict who will

go private), the improved productivity found in these earlier papers and shown in our before-after

analysis is not due to the change in the agency problems and capital market myopia of no longer

being a public firm. We therefore conclude that agency problems and capital market myopia in public

markets lead to no greater impact on productivity than that experienced by similar private firms.

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[43] Schoar A, 2002, “Effects of corporate diversification on productivity”, Journal of Finance 57 (6):

2379-2403.

[44] Shleifer, A., and Vishny, R., 1997. “A survey of corporate governance”, Journal of Finance 52:

737-783.

[45] Stein, J. C., 1989, “Efficient capital markets, inefficient firms: A model of myopic corporate

behavior”, Quarterly Journal of Economics 104, 655-669.

33

Data Appendix

The Census of Manufactures (CMF) covers all establishments is a quinquennial census that isundertaken in years 1977, 1982, 1987, 1992, 1997 and 2002. For the other years used in ouranalysis, we use data from the ASM that surveys: (i) All establishments with greater than (orequal to) 250 employees; (ii) All establishments of multi-unit firms; and (iii) a stratified random-ized sample of establishments with less than 250 employees. For certain small establishmentsin the CMF, the employment data is imputed based on reported payroll from administrativerecords data. Following the practice in the literature, such establishments (which are flaggedby an ‘Administrative Records’ dummy variable) are excluded from our analysis. As very few(less than 1%) of the going-private establishments belong to this category, this exclusion hasvery little impact on our sample size.

Key variables used in the analysis are as defined below. Deflators used for obtaining realvalues are taken from the NBER-CES manufacturing industry database (Becker and Gray 2009).

(i). Output measures

(a) Log real sales is defined as value of shipments deflated using 4-digit SIC industry-specific output deflators.

(b) Log real value added is defined as log of (real sales - real materials - real energy costs).

(ii). Input measures

(a) Log employment is the log of the total number of employees reported in the ASM-CMFdatabase.

(b) Log real materials is the log of the deflated cost of materials used.(c) Log real energy costs is the log of the deflated cost of fuel, electricity and other energy

sources used.(d) Log real capital is defined as the log the real depreciated capital stock. The real

depreciated capital stock is constructed using the perpetual inventory method. Thedepreciation rates (and deflators) used to construct the plant specific real depreciatedstructures and equipment stocks were taken from Becker and Gray, 2009.

(iii). Blundell-Bond system-GMM TFP measureBasic definitions of the productivity measures are provided in the text. Here we describeelaborate on the specific methodology used to define the Blundell-Bond total factor pro-ductivity measure (more detail is available in Blundell and Bond 2000, and Bond andSoderbom 2005).As in Blundell and Bond (2000), we assume a gross output production function with anAR1 component in the productivity term:

yit = βl.lit + βn.nit + βk.kit + βmmit + βeeit + ηi + νit +mit

νit = ρνit−1 + ϵit |ρ| < 1ϵit,mit ∼ MA(0)

where output and inputs are as defined in section 2.2. The model has a dynamic (commonfactor representation):

yit = πl.lit + π2.lit−1 + π3.nit + π4.nit−1 + π5.kit + π6.kit−1 + π7mit + π8mit−1

+π9eit + π10eit + π11yit−1 + η∗i + ωit

subject to 5 common factor restrictions: π2 = −π1 ∗π11, π4 = −π3 ∗π11, π6 = −π5 ∗π11,π8 = −π7 ∗ π11 and π10 = −π9 ∗ π11, and where η∗i = ηi(1− ρ). The standard Arellano-Bond moment (1991) conditions are

E[xit−j∆ωit] = 0 where xit = (lit, nit, kit,mit, eit, yit)

34

for j ≥ 3 (assuming ωit ∼ MA(1)). This allows the use of suitably lagged levels of thevariables as instruments, after the equation has been first differenced to eliminate fi, theplant specific fixed effects. Blundell and Bond (2000) show that by assuming input andoutput in first differences depend only on the history of the productivity shock till time tbut do not depend on the fixed effect fi, one can obtain additional moment conditionsthat use lagged first differences as valid instruments for the equation in levels which greatlyimprove upon the properties of the estimator. These conditions are:

E[∆xit−j(η∗i + ωit)] = 0 where xit = (lit, nit, kit,mit, enit, yit) (5)

for j=2 (assuming ωit ∼ MA(1)). Both sets of moment conditions can be exploited as alinear GMM estimator in a system containing both first-differenced and levels equations.Combining both sets of moment conditions provides the Blundell and Bond (2000) systemGMM estimator. The underlying production function parameter estimates are recoveredby imposing the common factor restrictions using a minimum distance estimator.In the literature on production function estimation, two innovative two-stage approacheswere suggested by Olley and Pakes (OP) (1996) and Levinsohn and Petrin (LP) (2003),based on using investment and other inputs as proxies to condition out endogenous partof the productivity in the first stage. However, these approaches have been critiqued byAckerberg, Caves and Frazer (2006). Importantly, Bond and Soderbom (2005) show thatthe Blundell and Blond (2000) estimator addresses a critique of the OP and LP approachesput forth by Ackerberg, Caves and Frazer (2006).

35

Table 1 Panel A: Sample characteristics, by years around the “going-private” decisionThe data used to construct this sample is taken from the Census of Manufactures (CMF) and the Annual Survey

of Manufactures (ASM). For each establishment in the going private sample, we include upto eight establishments

(based on data availability) that are closest in size (employment) to the going private establishment from within

the same 3-digit SIC industry, and belonging to the same age quartile as controls.

Years Total Number Number Total Number Numberfrom number of of matched number of going- of controlgoing of going-private control of private firmsprivate establishments establishments establishments firms firms

-6 13,368 2,189 11,179 7,210 490 6,720

-5 13,808 2,198 11,610 7,284 469 6,815

-4 15,907 2,480 13,427 8,368 447 7,921

-3 16,017 2,413 13,604 8,546 449 8,097

-2 17,483 2,652 14,831 9,410 440 8,970

-1 20,114 2,701 17,413 11,255 406 10,849

0 16,095 2,274 13,821 8,567 421 8,146

1 15,515 2,280 13,235 7,984 454 7,530

2 14,350 2,228 12,122 6,821 469 6,352

3 12,961 2,020 10,941 6,048 462 5,586

4 11,684 1,886 9,798 5,339 420 4,919

5 9,724 1,693 8,031 4,495 378 4,117

6 8,883 1,504 7,379 4,358 371 3,987

Total 185,909 28,518 157,391 95,685 5,676 90,009

36

Table 1 Panel B: Sample characteristics: Breakdown by acquirer typeWe classify the sample firms that went private into three categories: buyouts by (i) private operating firms, (ii)

private equity firms, and (iii) management. We source the classifications for these deals using newspaper reports

from Factiva. The residual category is unclassified. The category-types are non-exclusive, so that some deals are

classified under more than one type; hence the net total number is not the sum of the numbers in the sub-categories.

The data used to construct this sample is taken from the Census of Manufactures (CMF) and the Annual Survey

of Manufactures (ASM). For each establishment in the going private sample, we include upto eight establishments

(based on data availability) that are closest in size (employment) to the going private establishment from within

the same 3-digit SIC industry, and belonging to the same age quartile as controls.

Acquirer Total Number Number Total Number Numbertype number of of matched number of going- of control

of going-private control of private firmsestablishments establishments establishments firms firms

Unclassified 2,157 259 1,898 1,186 48 1,138

Management 5,966 819 5,147 3,426 112 3,314

Operating 9,056 1,207 7,849 5,020 188 4,832

Private equity 6,838 958 5,880 3,761 115 3,646

Net total 20,114 2,701 17,413 11,255 406 10,849

37

Table 1 Panel C: Characteristics of going private (target) establishments: Breakdown byacquirer typeThis table presents sample characteristics of the going private establishments. The figures in the last 3rows are demeaned using the 3 digit SIC industry mean for the sample.

Characteristics Going Unclassified Operating Private Management(based on Year -1 to Year -3) Private Sample Equity

N=7766 N=721 N=3524 N=2734 N=2373

Mean Mean Mean Mean Mean

Log (deflated capital) 8.575 8.430 8.699 8.481 8.563

Log (employment) 4.937 4.608 5.010 4.889 5.058

Labor Productivity 3.318 3.304 3.296 3.422 3.293

OLS TFP 3.618 3.571 3.606 3.683 3.621

Blundell-Bond TFP 2.426 2.422 2.405 2.456 2.432

Characteristics N=2701 N=259 N=1207 N=958 N=819

(based on Year -1 ) Mean Mean Mean Mean Mean

Number of Plants Per Firm 27.146 20.259 19.499 41.992 42.958

Number of Industries 7.401 4.259 5.843 10.795 12.247

Firm Sales ($, ’000s) 1576 327 1778 1964 2130

Labor Productivity (Industry demeaned) 0.056 0.045 0.028 0.130 0.059

OLS TFP (Industry demeaned) 0.040 0.055 0.025 0.075 0.033

Blundell-Bond TFP (Industry demeaned) -0.006 -0.016 -0.024 0.036 -0.013

38

Table 1 Panel D: Characteristics of acquirer establishments: Breakdown by acquirer typeThis table presents sample characteristics of the acquirer establishments. Because the acquirer forManagement takeovers is the management of the original firm, we exclude that category from this table.(For this category, the figures in Panel C above can be considered as applicable for acquirers as well.) Thefigures in the last 3 rows are demeaned using the 3 digit SIC industry mean for the sample.

Going Private Sample Unclassified Operating Private Equity

Acquirer Characteristics N=5088 N=212 N=2729 N=1918

(based on Year -1 to Year -3) Mean Mean Mean Mean

Log (deflated capital) 8.383 8.424 8.543 8.143

Log (employment) 4.661 4.941 4.694 4.482

Labor Productivity 3.529 3.023 3.616 3.56

OLS TFP 3.665 3.373 3.696 3.668

Blundell-Bond TFP 2.519 2.408 2.524 2.533

Acquirer Characteristics N=1756 N=73 N=948 N=657

(based on Year -1 ) Mean Mean Mean Mean

Number of Plants Per Firm 44.726 10.699 28.082 74.906

Number of Industries 10.380 4.849 8.028 14.327

Firm Sales ($, ’000s) 2150 271 2210 2414

Labor Productivity (Industry demeaned) 0.195 -0.167 0.2163 0.240

OLS TFP (Industry demeaned) 0.094 -0.070 0.1 0.112

Blundell-Bond TFP (Industry demeaned) 0.059 -0.046 0.057 0.078

39

Table 2 Panel A: Changes in productivity around the “going-private” decision: Before-After(BA) and Difference-in-Differences (DID) specificationsThis table specifications 1-3 presents regression results for each of the productivity measures for the sample of

establishments of firms that went private. The data used to construct this sample is taken from the Census of

Manufactures (CMF) and the Annual Survey of Manufactures (ASM). Dummy variables LR PRE equals 1 for

years -6 to -4 from going private and zero otherwise, SR PRE equals 1 for years -3 to -1 from going private

and zero otherwise, SR POST equals 1 for years 0 to 3 from going private and zero otherwise, and LR POST

equals 1 for years 4 to 6 from going private and zero otherwise. P-values based on standard errors clustered by

establishment are in parentheses. This table specifications 4-6 presents difference-in-differences regression results

for each of the different productivity measures. For each establishment in the going private sample, we include

upto eight establishments (based on data availability) that are closest in size (employment) to the going private

establishment from within the same 3-digit SIC industry, and belonging to the same age quartile as controls.

P-values based on standard errors clustered by industry-size-age cells are in parentheses.

Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP

Before-After DiD

1 2 3 4 5 6

LR PRE 3.237 3.593 2.389 0.066 0.030 0.000(0.000) (0.000) (0.000) (0.000) (0.002) (0.983)

SR PRE 3.342 3.627 2.426 0.080 0.037 0.005(0.000) (0.000) (0.000) (0.000) 0.000 (0.543)

SR POST 3.405 3.659 2.463 0.090 0.027 (0.001)(0.000) (0.000) (0.000) (0.000) (0.005) (0.930)

LR POST 3.414 3.691 2.477 0.085 0.026 0.006(0.000) (0.000) (0.000) (0.000) (0.028) (0.592)

CHANGES RELATIVE TO SR PRESR POST - SR PRE 0.063 0.032 0.037 0.011 (0.011) (0.006)

(0.000) (0.000) (0.000) (0.510) (0.202) (0.479)

LR POST - SR PRE 0.072 0.064 0.051 0.005 (0.011) 0.001(0.000) (0.000) (0.000) (0.817) (0.374) (0.924)

TEST FOR PRE-EXISTING TREND

SR PRE - LR PRE 0.105 0.034 0.037 0.014 0.008 0.005(0.000) (0.000) (0.000) (0.362) (0.342) (0.492)

Fixed effects Plant Plant Plant Industry-size-age-yearNumber of Observations 28,518 28,518 28,518 185,909 185,909 185,909

40

Table 2 Panel B: Changes in productivity around the “going-private” decision: BA and DIDspecifications, excluding sold plants (from both going-private and control samples)The data used to construct this sample excludes all sold plants (i.e., those that underwent a change in ownership

after the going private date) in both the treatment and control samples and is taken from the Census of

Manufactures (CMF) and the Annual Survey of Manufactures (ASM). This table specifications 1-3 presents

regression results for each of the productivity measures for the sample of establishments of firms that went private.

Dummy variables LR PRE equals 1 for years -6 to -4 from going private and zero otherwise, SR PRE equals 1

for years -3 to -1 from going private and zero otherwise, SR POST equals 1 for years 0 to 3 from going private

and zero otherwise, and LR POST equals 1 for years 4 to 6 from going private and zero otherwise. P-values

based on standard errors clustered by establishment are in parentheses. This table specifications 4-6 presents

difference-in-differences regression results for each of the different productivity measures. For each establishment

in the going private sample, we include upto eight establishments (based on data availability) that are closest in

size (employment) to the going private establishment from within the same 3-digit SIC industry, and belonging

to the same age quartile as controls. P-values based on standard errors clustered by industry-size-age cells are in

parentheses.

Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP

Before-After DiD

1 2 3 4 5 6

LR PRE 3.250 3.619 2.401 0.070 0.033 0.000(0.013) (0.006) (0.006) (0.023) (0.013) (0.011)

SR PRE 3.372 3.656 2.444 0.088 0.041 0.003(0.010) 0.005 (0.005) (0.021) (0.012) (0.011)

SR POST 3.439 3.689 2.484 0.097 0.030 -0.005(0.008) 0.005 (0.004) (0.023) (0.013) (0.011)

LR POST 3.463 3.718 2.507 0.101 0.033 0.010(0.008) (0.005) (0.004) (0.030) (0.016) (0.016)

CHANGES RELATIVE TO SR PRESR POST - SR PRE 0.189 0.070 0.083 0.009 (0.010) (0.008)

(0.019) (0.010) (0.009) (0.679) (0.367) (0.450)

LR POST - SR PRE 0.213 0.099 0.106 0.013 (0.008) 0.007(0.025) (0.013) (0.012) (0.690) (0.655) (0.663)

TEST FOR PRE-EXISTING TRENDSR PRE - LR PRE 0.122 0.037 0.043 0.018 0.008 0.003

(0.016) (0.007) (0.007) (0.384) (0.468) (0.746)

Fixed effects Plant Plant Plant Industry-size-age-year

Number of Observations 20,788 20,788 20,788 139,826 139,826 139,826

41

Table 3 Panel A: Changes in productivity measures around the “going-private” decision: DIDspecifications using alternative propensity score and Past TFP matched control groupsThis table presents 3 regression results for each of three productivity measures for the sample of establishments

of firms that went private. In Columns 1 to 2, for each establishment in the going private sample, we construct

the closest propensity to go private score matched firm(s) but did not go private, as control firm(s). We use the

firm specific control variables at the time of the IPO to estimate the propensity to go private as in Bharath and

Dittmar (2010). Since these control variables are not available for all firms (whose establishments we consider

in the regression), the number of establishments of firms that went private drops to 22,488 (from 28,518 in

table 2) in these propensity-matched estimations. In specifications 4-6 we match establishments based on past

TFP (using the Blundell-Bond measure) within the same industry and age quartile. The data used to construct

these samples is taken from the Census of Manufactures (CMF) and the Annual Survey of Manufactures (ASM).

Dummy variables LR PRE equals 1 for years -6 to -4 from going private and zero otherwise, SR PRE equals 1 for

years -3 to -1 from going private and zero otherwise, SR POST equals 1 for years 0 to 3 from going private and

zero otherwise, and LR POST equals 1 for years 4 to 6 from going private and zero otherwise. P-values based

on standard errors clustered by industry-propensity cell year (specifications 1-3) and industry-past TFP cell year

(specifications 4-6) are in parentheses.

Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP

1 2 3 4 5 6

LR PRE -0.052 -0.018 -0.002 0.0531 0.0429 (0.005)

(0.238) (0.489) (0.942) (0.015) (0.008) (0.007)

SR PRE -0.004 0.004 0.006 0.085 0.0763 0.002

(0.916) (0.874) (0.769) (0.012) (0.006) (0.004)

SR POST -0.030 -0.026 -0.015 0.0998 0.058 (0.002)

(0.445) (0.273) (0.484) (0.015) (0.008) (0.007)

LR POST 0.036 0.033 0.037 0.098 0.049 (0.006)

(0.509) (0.292) (0.189) (0.021) (0.011) (0.011)

CHANGES RELATIVE TO SR PRE

SR POST - SR PRE -0.026 -0.030 -0.021 0.015 (0.019) (0.0038)

(0.497) (0.187) (0.310) (0.319) (0.012) (0.584)

LR POST - SR PRE 0.040 0.029 0.031 0.0122 (0.0271) (0.0036)

(0.488) (0.366) (0.292) (0.568) (0.017) (0.490)

TEST FOR PRE-EXISTING TREND

SR PRE - LR PRE 0.048 0.022 0.007 (0.032) (0.033) (0.007)

(0.246) (0.339) (0.720) (0.025) (0.000) (0.245)

Fixed effects Industry-propensity cell-year Industry-past TFP-age-year

Number of Observations 55,358 55,358 55,358 179,043 179,043 179,043

42

Table 3 Panel B: Changes in productivity measures around the “going-private” decision:DID specifications using alternative propensity score and Past TFP matched control groups,excluding sold plantsThe data used to construct this sample excludes all sold plants (i.e., those that underwent a change in ownership

after the going private date) in both the treatment and control samples. For each establishment in the going

private sample, we construct the closest propensity to go private score matched firm(s) but did not go private, as

control firm(s). We use the firm specific control variables at the time of the IPO to estimate the propensity to

go private as in Bharath and Dittmar (2010). Since these control variables are not available for all firms (whose

establishments we consider in the regression) in our sample, the number of establishments of firms that went

private drops in these estimations. In specifications 4-6 we match establishments based on past TFP (using the

Blundell-Bond measure) within the same industry and age quartile. The data used to construct these samples is

taken from the Census of Manufactures (CMF) and the Annual Survey of Manufactures (ASM). Dummy variables

LR PRE equals 1 for years -6 to -4 from going private and zero otherwise, SR PRE equals 1 for years -3 to -1

from going private and zero otherwise, SR POST equals 1 for years 0 to 3 from going private and zero otherwise,

and LR POST equals 1 for years 4 to 6 from going private and zero otherwise. P-values based on standard errors

clustered by industry-propensity cell year (specifications 1-3) and industry-past TFP cell year (specifications 4-6)

are in parentheses.

Labor OLS Blundell- Labor OLS Blundell-

productivity TFP Bond TFP productivity TFP Bond TFP

1 2 3 4 5 6

LR PRE -0.034 -0.031 -0.002 0.0486 0.0479 -0.010

(0.072) (0.045) (0.039) (0.020) (0.010) (0.008)

SR PRE -0.007 0.010 0.003 0.105 0.0905 0.001

(0.059) (0.036) (0.031) (0.015) (0.008) (0.005)

SR POST -0.025 -0.019 -0.020 0.1080 0.066 -0.005

(0.068) (0.042) (0.038) (0.021) (0.011) (0.009)

LR POST 0.023 0.034 0.027 0.111 0.060 -0.005

(0.094) (0.060) (0.055) (0.028) (0.015) (0.015)

CHANGES RELATIVE TO SR PRE

SR POST - SR PRE -0.019 -0.029 -0.023 0.003 -0.025 -0.007

(0.776) (0.457) (0.513) (0.876) (0.015) (0.447)

LR POST - SR PRE 0.030 0.024 0.023 0.006 -0.030 -0.006

(0.758) (0.703) (0.677) (0.821) (0.058) (0.674)

TEST FOR PRE-EXISTING TREND

SR PRE - LR PRE 0.027 0.041 0.005 0.056 0.043 0.011

(0.695) (0.282) (0.883) (0.003) (0.000) (0.139)

Fixed effects Industry-propensity cell-year Industry-past TFP-age-year

Number of Observations 36,659 36,659 36,659 136,218 136,218 146,220

43

Table 4: Changes in capital and employment around the “going-private” decision : Before-Afterand Difference-in-Differences Specifications

This table presents before-after and difference-in-differences regression results for capital and employment. In

DID1, for each establishment in the going private sample, we include upto eight establishments (based on data

availability) that are closest in size (employment) to the going private establishment from within the same 3-digit

SIC industry, and belonging to the same age quartile as controls. In DID2, for each establishment in the going

private sample, we include upto eight establishments (based on data availability) that are closest in past TFP

to the going private establishment from within the same 3-digit SIC industry, and belonging to the same age

quartile as controls. In DID3, we include upto eight establishments within the same 3-digit SIC industry closest in

propensity to go private (but whose owner firms did not go private), as control establishments. The data used to

construct this sample is taken from the Census of Manufactures (CMF) and the Annual Survey of Manufactures

(ASM). Dummy variables LR PRE equals 1 for years -6 to -4 from going private and zero otherwise, SR PRE

equals 1 for years -3 to -1 from going private and zero otherwise, SR POST equals 1 for years 0 to 3 from

going private and zero otherwise, and LR POST equals 1 for years 4 to 6 from going private and zero otherwise.

P-values based on standard errors clustered by plant establishments (before-after), industry-size-age cells (DID1),

industry-past TFP-age cells (DID2) and industry-propensity cells (DID3) are in parentheses.

Log deflated capital Log employment

Before-After DID1 DID2 DID3 Before-After DID1 DID2 DID3

1a 1b 1c 1d 2a 2b 2c 2d

LR PRE 8.642 0.181 0.369 -0.223 5.033 0.043 0.204 -0.134

(0.000) (0.000) (0.034) (0.012) (0.000) (0.000) (0.025) (0.033)

SR PRE 8.659 0.195 0.657 -0.124 4.994 0.021 0.423 -0.054

(0.000) (0.000) (0.033) (0.117) (0.000) (0.000) (0.024) (0.345)

SR POST 8.684 0.092 0.460 -0.176 4.936 0.001 0.299 -0.076

(0.000) (0.000) (0.035) (0.038) (0.000) (0.929) (0.026) (0.229)

LR POST 8.722 0.044 0.293 -0.278 4.905 -0.011 0.166 -0.123

(0.000) (0.078) (0.042) (0.005) (0.000) (0.459) (0.031) (0.101)

CHANGES RELATIVE TO SR PRESR POST - SR PRE 0.025 -0.103 -0.197 -0.052 -0.058 -0.021 -0.124 -0.022

(0.001) (0.000) (0.000) (0.405) (0.000) (0.032) (0.000) (0.650)

LR POST - SR PRE 0.064 -0.151 -0.364 -0.154 -0.089 -0.033 -0.257 -0.069

(0.000) (0.000) (0.000) (0.090) (0.000) (0.038) (0.000) (0.321)

TEST FOR PRE-EXISTING TRENDSR PRE - LR PRE 0.017 0.014 0.288 0.099 -0.039 -0.021 0.219 0.08

(0.009) (0.369) (0.000) (0.147) (0.007) (0.012) (0.000) (0.099)

Fixed effects Plant Cell-year Cell-year Cell-year Plant Cell-year Cell-year Cell-year

Number of Observations 28,518 185,909 179,043 55,358 28,518 185,909 179,043 55,358

44

Table

5Panel

A:SummaryStatisticsonExitRates,

byIndustry

oftheGoingPriva

teSample

The3-

(6-)

year

Exitrate

isthemeanof

thecorrespon

dingExitdummy,whichequalson

eiftheplantexited

(was

sold

orclosed)in

thenext

three(six)yearsandzero

otherwise.

The3-

(6-)

year

Soldrate

isthemeanof

thecorrespon

dingSolddummywhichequalson

eiftheplant

was

sold

inthenextthree(six)yearsandzero

otherwise.

The3-

(6-)

year

Closedrate

isthemeanof

thecorrespon

dingCloseddummywhich

equalson

eiftheplantwas

shutdow

nin

thenextthree(six)yearsandzero

otherwise.

(Thesevariablesareundefined

(missing)

foraplantfor

timeperiodsafteritisshutdow

n).

Thevariablesaredefined

usingtheLon

gitudinal

BusinessDatabase;

dataispresentedforsub-industries

within

manufacturingon

ly.

SIC

Industry

No.

ofNo.

of3year

3year

3year

No.

of6year

6year

6year

firm

sPlants

ExitRate

SoldRate

ClosedRate

Plants

ExitRate

SoldRate

ClosedRate

20Food&

KindredProductsMfrs

3577

539

.9%

20.4%

19.5%

683

54.2%

26.2%

28.0%

22Textile

Mill

ProductsMfrs

1733

828

.7%

12.4%

16.3%

293

50.5%

23.2%

27.3%

23Apparel

&18

289

27.3%

4.5%

22.8%

274

50.0%

7.7%

42.3%

Other

Finished

Products-Mfrs

24,25

Lumber

&WoodProds

2127

755

.2%

31.8%

23.5%

245

76.3%

41.2%

35.1%

26Pap

er&

1218

158

.6%

40.9%

17.7%

178

69.1%

45.5%

23.6%

Allied

ProductsMfrs

27PrintingPublishing&

Allied

Industries

3053

236

.1%

15.6%

20.5%

467

60.6%

26.8%

33.8%

28,29

Chem

icals&

Petroleum

Refining

2457

135

.0%

13.7%

21.4%

549

50.3%

20.0%

30.2%

30Rubber

&MiscellaneousPlasticsMfrs

3250

937

.9%

21.2%

16.7%

479

53.0%

26.7%

26.3%

31Leather

&13

355

34.4%

12.4%

22.0%

313

53.0%

21.4%

31.6%

Leather

ProductsMfrs

33PrimaryMetal

Industries

Mfrs

1721

737

.3%

16.6%

20.7%

202

50.5%

21.8%

28.7%

34FabricatedMetal

ProductsMfrs

3854

344

.0%

20.4%

23.6%

507

57.8%

26.8%

31.0%

35Industrial

&48

442

44.1%

17.9%

26.2%

413

62.0%

26.2%

35.8%

Com

mercial

MachineryMfrs

36Electronic

&47

350

46.0%

18.0%

28.0%

333

64.3%

27.3%

36.9%

Other

ElectricalEquip

Mfr

37Transportation

EquipmentMfrs

929

435

.7%

15.6%

20.1%

282

47.9%

21.3%

26.6%

38Measuring&

3320

044

.0%

19.5%

24.5%

179

62.0%

29.1%

33.0%

AnalyzingInstruments-M

frs

39Miscellaneous

1610

132

.7%

9.9%

22.8%

8757

.5%

14.9%

42.5%

ManufacturingIndsMfrs

45

Table

5Panel

B:SummaryStatisticsonExitRatesbyperiodandbyIndustry

Typ

eThevariablesaredefined

usingtheLon

gitudinal

BusinessDatabase.

See

notes

toPanelAfordefinitionof

Exit,SoldandClosedRates.‘M

PP’

refers

tofigu

resfrom

Table

3of

Maksimovic,PhillipsandPrabhala,

2011

.Industry

types

aredefined

asper

Maksimovic

andPhillips(2008).

Sam

ple

No.

ofNo.

of3year

3year

3year

No.

of6year

6year

6year

firm

sPlants

ExitRate

SoldRate

ClosedRate

Plants

ExitRate

SoldRate

ClosedRate

GoingPrivate

Firms

1258

8133

336

.5%

8.6%

27.9%

7085

457

.9%

14.2%

43.7%

Con

trol

Firms

8665

512

8340

34.2%

7.2%

27.0%

1126

1454

.1%

10.9%

43.2%

MPP-MergerFirms(Target)

1289

345

.6%

27.0%

18.6%

MPP-MergerFirms(C

ontrolsforTarget)

12.3%

9.0%

3.3%

GoingPrivate

Firms-Not

Classified

113

6375

36.5%

8.6%

27.9%

6214

54.3%

12.8%

41.5%

Con

trol

Firms-Not

Classified

6663

1005

235

.4%

7.7%

27.7%

9824

50.8%

11.2%

39.7%

GoingPrivate

Firms-Managem

ent

353

1962

130

.0%

8.9%

21.1%

1705

652

.1%

15.5%

36.6%

Con

trol

Firms-Managem

ent

2322

132

635

32.4%

7.6%

24.9%

2869

352

.3%

11.4%

41.0%

GoingPrivate

Firms-Operating

721

4524

336

.4%

7.9%

28.6%

3849

059

.3%

13.8%

45.5%

Con

trol

Firms-Operating

4777

071

563

35.1%

7.2%

28.0%

6226

455

.5%

10.8%

44.6%

GoingPrivate

Firms-Private

Equity

278

2568

241

.1%

12.8%

28.3%

2217

960

.4%

17.3%

43.1%

Con

trol

Firms-Private

Equity

2732

438

957

32.9%

7.0%

25.9%

3287

254

.0%

10.9%

43.1%

Bytimeperiod:1980s

GoingPrivate

Firms

592

4443

037

.2%

11.7%

25.5%

4443

053

.5%

16.8%

36.7%

Con

trol

Firms

4816

973

154

33.8%

7.0%

26.8%

7315

449

.5%

9.7%

39.8%

MPP-MergerFirms(Target)

6710

49.7%

30.3%

19.4%

MPP-MergerFirms(C

ontrolsforTarget)

14.5%

10.8%

3.7%

Bytimeperiod:1990s

GoingPrivate

Firms

715

3957

435

.5%

4.9%

30.6%

2909

563

.1%

9.3%

53.8%

Con

trol

Firms

4184

759

690

34.8%

7.6%

27.2%

4396

461

.2%

12.8%

48.4%

MPP-MergerFirms(Target)

6183

40.9%

23.2%

17.6%

MPP-MergerFirms(C

ontrolsforTarget)

10.2%

7.3%

2.9%

ByIndustry

Typ

e:Growth

GoingPrivate

Firms

2362

338

.4%

18.9%

19.4%

593

53.0%

28.7%

24.3%

Con

trol

Firms

558

1024

30.6%

12.5%

18.1%

966

44.8%

19.3%

25.6%

ByIndustry

Typ

e:Consolid

ating

GoingPrivate

Firms

260

3154

39.4%

19.6%

19.8%

2831

55.6%

26.5%

29.1%

Con

trol

Firms

558

5419

27.6%

12.4%

15.2%

4814

44.8%

19.6%

25.3%

ByIndustry

Typ

e:Tec

hnologicalChange

GoingPrivate

Firms

6712

5437

.6%

13.2%

24.4%

1173

56.3%

20.0%

36.2%

Con

trol

Firms

1086

2121

31.4%

10.2%

21.2%

1970

48.2%

14.3%

33.9%

ByIndustry

Typ

e:Dec

lining

GoingPrivate

Firms

6091

241

.1%

18.2%

22.9%

856

61.7%

26.2%

35.5%

Con

trol

Firms

1133

1590

34.7%

11.4%

23.3%

1497

51.0%

15.6%

35.4%

46

Table 6: Establishment exit after the “going-private” decision: Hazard and propensity analysis

This table presents exponential and competing risks model (Panel A) and linear propensity model (Panel B) results.

In Column 1 of Panel B, the 3- (6-) year Exit dummy is a variable that equals one if the plant exited (was sold

or closed) in the next three (six) years and zero otherwise. In Column 2 of Panel B, the 3- (6-) year Sold dummy

is a variable that equals one if the plant was sold in the next three (six) years and zero otherwise. In Column 2

of Panel B, the 3- (6-) year Closed dummy is a variable that equals one if the plant was shut down in the next

three (six) years and zero otherwise. (These variables are undefined (missing) for a plant for time periods after it

is shut down). For both panels, for each establishment in the going private sample, we include two establishments

that are closest in size (employment) to the going private establishment from within the same 3-digit SIC industry

and age quartile as controls. In both Panel A and Panel B, the analysis is done using time-invariant explanatory

variables, so the data has one observation for each of the gone private and control establishments. In Panel C and

D, exclude industry fixed effects in order to explore the role of industry-level variables. Capacity utilization measure

is taken from Gorodnichenko and Shapiro (2011). Industry concentration is measured as in Kovenock and Phillips

(1997) as the market share of the top four plants in the industry. Change in output demand is defined as aggregate

change in industry output. Industry classifications used in Panel D are based on definitions in Maksimovic and

Phillips (2008). The data used to construct this sample is taken from the Longitudinal Business Database (LBD).

P-values based on standard errors clustering by control group cells are in parentheses.

Panel A: Hazard and Exponential Exponential Exponential Competing RisksCompeting Risks Models model model model model

Exit Sold Closed Sold Closed

1 2 3 4a 4b

Log employment -0.110 0.216 -0.217 0.191 0.176

(0.000) (0.000) (0.000) (0.000) (0.000)

Age -0.023 0.007 -0.027 -0.001 -0.002

(0.000) (0.000) (0.000) 0.317 0.046

Gone-private dummy 0.142 0.285 0.059 0.261 0.267

(0.000) (0.000) (0.000) (0.000) (0.000)

Gone-private dummy hazard ratio 1.153 1.330 1.061 1.298 1.306

Control group Industry-size-age matched Industry-size-age matched

Fixed Effects Industry, Year Industry, Year Industry, Year Industry, Year Industry, Year

Observations 203,311 183,329 203,311 203,541 203,311

Panel B: OLS with Cell - Year 3-year Outcome dummy 6-year Outcome dummy

Fixed Effects Exit Sold Closed Exit Sold Closed

1 2 3 1 2 3

Gone-private dummy 0.031 0.015 0.015 0.051 0.043 0.008

(0.000) (0.000) (0.000) (0.000) (0.000) (0.008)

Fixed effects Industry-size-age matched cell - Year Industry-size-age matched cell - Year

Number of Observations 209,673 209,673 209,673 183,468 183,468 183,468

47

Table 6 (..ctd): Establishment exit after the “going-private” decision: Hazard and propensityanalysis

Panel C 3-year outcome dummy 6-year outcome dummy

Exit Sold Closed Exit Sold Closed

1 2 3 1 2 3

Gone-private dummy 0.108 0.050 0.058 0.093 0.047 0.046

(0.000) (0.000) (0.000) (0.000) (0.007) (0.004)

Labor Productivity -0.036 0.001 -0.037 -0.035 0.009 -0.044

(0.000) (0.895) (0.000) (0.002) (0.344) (0.000)

Firm Employment -3.24E-07 -1.77E-07 -1.47E-07 -3.17E-07 -2.26E-07 -9.06E-08

(0.002) (0.006) (0.090) (0.009) (0.006) (0.382)

Number of Plants Owned by Firm 0.00004 0.00003 0.00002 0.00003 0.00003 0.00000

(0.017) (0.051) (0.200) (0.109) (0.075) (0.885)

Capacity Utilization -0.00019 0.00013 -0.00032 -0.00085 -0.00014 -0.00071

(0.656) (0.710) (0.369) (0.069) (0.739) (0.115)

Change in Output Demand -0.262 0.072 -0.333 -0.023 0.114 -0.137

(0.030) (0.502) (0.001) (0.871) (0.366) (0.287)

Lagged Industry Concentration 0.093 0.182 -0.0891 -0.0675 0.157 -0.2246

(0.489) (0.146) (0.456) (0.631) (0.294) (0.105)

Fixed Effects Year Year Year Year Year Year

Observations 3,134 3,134 3,134 2,802 2,802 2,802

Panel D 3-year outcome dummy 6-year outcome dummy

Exit Sold Closed Exit Sold Closed

1 2 3 1 2 3

Gone-private dummy 0.121 0.068 0.053 0.110 0.071 0.039

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Labor Productivity -0.031 0.004 -0.035 -0.034 0.012 -0.046

(0.000) (0.415) (0.000) (0.000) (0.031) (0.000)

Firm Employment -4.48E-07 -2.88E-07 -1.60E-07 -4.59E-07 -3.21E-07 -1.38E-07

(0.000) (0.000) (0.008) (0.000) (0.000) (0.065)

Number of Plants Owned by Firm 0.00006 0.00004 0.00001 0.00006 0.00005 0.00001

(0.000) (0.000) (0.230) (0.000) (0.000) (0.370)

Consolidating Industry 0.006 0.014 -0.007 0.019 0.004 0.015

(0.691) (0.309) (0.567) (0.276) (0.824) (0.358)

Technological Change Industry -0.007 0.005 -0.011 0.006 -0.004 0.011

(0.731) (0.773) (0.492) (0.768) (0.820) (0.594)

Declining Industry 0.024 -0.001 0.025 0.059 -0.010 0.070

(0.256) (0.970) (0.177) (0.011) (0.628) (0.002)

Fixed Effects Year Year Year Year Year Year

Observations 9,380 9,380 9,380 8,363 8,363 8,363

48

Table

7:Exithazard

after

the“going-priva

te”dec

ision:Tests

fordifferen

tialtargeting

Thistable

presents

expon

ential

model

resultsof

theduration

toexit,sale

andclosure

measuresof

thesample

ofestablishments

offirm

sthat

wentprivate.

Incolumns1,

2and3,

foreach

establishmentin

thego

ingprivatesample,weincludeupto

eigh

testablishments

(based

ondata

availability)that

areclosestin

size

(employm

ent)

tothego

ingprivateestablishmentfrom

within

thesame3-digitSIC

industry,andbelon

ging

tothesameagequartile

ascontrols.

Thedatausedto

construct

thissample

istakenfrom

theCensusof

Manufactures(C

MF),

theAnnual

Survey

ofManufactures(A

SM)andthelongitudinal

businessdatabase(LBD).

P-values

based

onstandarderrors

clustered

bycontrol

grou

p

cells

arein

parentheses.

Panel

A:Hazard

model

withIndustry,

Exit

Sold

Closed

Exit

Sold

Closed

Exit

Sold

Closed

Yea

rFixed

Effec

ts1

23

12

31

23

ProductivityMeasure

=Lab

orProductivity

OLSFETFP

Blundell-Bon

dTFP

Gon

e-Private

Dummy

0.97

81.10

90.57

40.57

51.01

3-0.229

0.87

10.950

0.394

(0.000

)(0.000

)(0.041

)(0.005

)(0.000

)(0.412

)(0.000

)(0.000)

(0.127)

ProductivityMeasure

-0.024

0.13

5-0.163

-0.048

0.16

3-0.224

0.00

80.165

-0.110

(0.408

)(0.001

)(0.000

)(0.307

)(0.018

)(0.000

)(0.880

)(0.028)

(0.116)

Gon

e-privatedummyXProductivityMeasure

-0.115

-0.141

-0.065

-0.054

-0.172

0.12

4-0.198

-0.224

-0.077

(0.003)

(0.008)

(0.237

)(0.343

)(0.029)

(0.107

)(0.005)

(0.020)

(0.446)

Observations

9,52

48,61

69,52

49,52

48,61

69,52

49,52

48,616

9,524

Con

trols

Employm

ent,age

Fixed

effects

Industry,Year

Panel

B:OLSwithCell-Yea

rExit

Sold

Closed

Exit

Sold

Closed

Exit

Sold

Closed

Fixed

Effec

ts(3

year

outcomes)

12

31

23

12

3

ProductivityMeasure

=Lab

orProductivity

OLSFETFP

Blundell-Bon

dTFP

Gon

e-Private

Dummy

0.34

00.14

80.19

10.28

70.16

00.12

70.38

10.273

0.108

(0.001

)(0.048

)(0.009

)(0.007

)(0.057

)(0.104

)(0.000

)(0.000)

(0.097)

ProductivityMeasure

-0.024

-0.002

-0.022

-0.030

-0.011

-0.019

-0.015

0.004

-0.018

(0.134

)(0.856

)(0.067

)(0.230

)(0.563

)(0.317

)(0.579

)(0.849)

(0.368)

Gon

e-privatedummyXProductivityMeasure

-0.040

-0.013

-0.027

-0.044

-0.022

-0.022

-0.102

-0.077

-0.025

(0.035)

(0.353

)(0.054)

(0.129

)(0.339

)(0.295

)(0.003)

(0.003)

(0.317)

Observations

9,64

69,64

69,64

69,64

69,64

69,64

69,64

69,646

9,646

Fixed

effects

Industry-size-agematched

cell-year

Fixed

Effects

49

Table

8Panel

A:Changes

inproductivity:

Differen

ce-in-D

ifferen

cessp

ecifica

tionsbreakdownbyacq

uirer

type

This

table

presents

testsof

differencesin

productivitybased

onseparateregression

sforeach

acquirer

typeidentified

inTable

1,Panel

B.

For

each

establishmentin

thego

ingprivatesample,weincludeupto

eigh

testablishments

(based

ondataavailability)that

areclosestin

size

(employm

ent)

tothego

ingprivateestablishmentfrom

within

thesame3-digit

SIC

industry,andbelon

gingto

thesameagequartile

ascontrols.

Column1includes

allestablishments;in

Column2,

thesample

forboththego

ingprivateandmatched

control

establishments

excludes

allsold

plants

(i.e.,

thosethat

underwentachange

inow

nership

afterthego

ingprivatedate).Thedatausedto

construct

this

sample

istakenfrom

theCensusof

Manufactures(C

MF)andtheAnnual

Survey

ofManufactures(A

SM).

DummyvariablesLR

PREequals

1foryears-6

to-4

from

delistingandzero

otherwise,

SR

PREisadummyvariable

that

equals1foryears-3

to-1

from

delistingandzero

otherwise,

SR

POST

equals1foryears0to

3from

delistingandzero

otherwise,

andLR

POST

equals1foryears4to

6from

delistingand

zero

otherwise.

Standarderrors

clustered

bycontrol

grou

pcells

areusedto

assess

theP-values

(inparentheses)forthedifferenttests.

Unclassified

Managem

ent

Operating

Priva

teEquity

12

12

12

12

Laborproductivity

Shortrunchange

(SR

POST

-SR

PRE)

-0.003

0.01

40.00

20.01

60.01

70.01

2-0.002

-0.004

(0.968

)(0.877

)(0.931

)(0.650

)(0.501

)(0.722

)(0.927)

(0.887)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.049

-0.111

-0.019

-0.012

0.02

40.04

60.003

0.021

(0.582

)(0.366

)(0.651

)(0.822

)(0.473

)(0.335

)(0.932)

(0.706)

Pre-existingtrend(SR

PRE-LR

PRE)

0.08

00.09

0-0.005

-0.002

0.01

10.01

00.008

0.020

(0.143

)(0.221

)(0.856

)(0.955

)(0.628

)(0.736

)(0.785)

(0.581)

OLSTFP

Shortrunchange

(SR

POST

-SR

PRE)

-0.061

-0.048

-0.022

-0.018

0.00

20.00

0-0.009

-0.006

(0.091)

(0.377

)(0.092)

(0.316

)(0.859

)(0.998

)(0.468)

(0.718)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.095

-0.132

-0.019

-0.020

0.01

70.03

8-0.018

-0.022

(0.055)

(0.060)

(0.356

)(0.493

)(0.319

)(0.125

)(0.369)

(0.441)

Pre-existingtrend(SR

PRE-LR

PRE)

0.061

0.068

-0.008

-0.010

0.01

00.00

90.001

0.010

(0.053)

(0.106)

(0.578

)(0.592

)(0.357

)(0.557

)(0.962)

(0.616)

BlundellBondTFP

Shortrunchange

(SR

POST

-SR

PRE)

-0.049

-0.050

-0.005

-0.001

-0.003

-0.009

-0.003

0.001

(0.174

)(0.347

)(0.655

)(0.962

)(0.839

)(0.585

)(0.833)

(0.948)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.080

-0.104

0.00

50.01

10.02

20.043

0.000

0.005

(0.101

)(0.133

)(0.810

)(0.691

)(0.176

)(0.070)

(0.985)

(0.867)

Pre-existingtrend(SR

PRE-LR

PRE)

0.061

0.04

5-0.008

-0.017

0.01

00.00

20.001

0.009

(0.053)

(0.209

)(0.578

)(0.330

)(0.357

)(0.921

)(0.962)

(0.595)

Sam

ple

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

Number

ofob

servations

18,683

13,947

56,753

42,209

84,020

62,917

62,795

47,249

Fixed

effects

Industry-size-age-year

50

Table

8Panel

B:Changes

inca

pitalandem

ploym

entaroundthe“going-priva

te”dec

ision:Differen

ce-in-D

ifferen

ces

specifica

tionsbreakdownbyacq

uirer

type

This

table

presents

resultsforthetestsof

changesin

capital

andem

ploym

entover

time,

based

onseparateregression

sforeach

acquirer

typeidentified

inTable

1,Panel

B.For

each

establishmentin

thego

ingprivatesample,weincludeupto

eigh

testablishments

(based

ondata

availability)that

areclosestin

size

(employm

ent)

tothego

ingprivateestablishmentfrom

within

thesame3-digitSIC

industry,andbelon

ging

tothesameagequartile

ascontrols.

Thedatausedto

construct

this

sample

istakenfrom

theCensusof

Manufactures(C

MF)andthe

Annual

Survey

ofManufactures(A

SM).DummyvariablesLR

PREequals1foryears-6

to-4

from

goingprivateandzero

otherwise,

SR

PRE

equals1foryears-3

to-1

from

goingprivateandzero

otherwise,

SR

POST

equals1foryears0to

3from

goingprivateandzero

otherwise,

andLR

POST

equals1foryears4to

6from

goingprivateandzero

otherwise.

Standarderrors

clustered

byindustry-size-agegrou

psareused

toassess

theP-values

(inparentheses)forthedifferenttests.

Unclassified

Managem

ent

Operating

Priva

teEquity

12

12

12

12

Logdefl

atedCapital

Shortrunchange

(SR

POST

-SR

PRE)

-0.158

-0.200

-0.122

-0.122

-0.079

-0.068

-0.110

-0.124

(0.010)

(0.015)

(0.000)

(0.001)

(0.001)

(0.057)

(0.000)

(0.000)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.126

-0.220

-0.217

-0.243

-0.116

-0.083

-0.210

-0.262

(0.136)

(0.068)

(0.000)

(0.000)

(0.001)

(0.102)

(0.000)

(0.000)

Pre-existingtrend(SR

PRE-LR

PRE)

0.08

30.07

60.03

50.03

1-0.004

-0.017

0.012

0.023

(0.157

)(0.342

)(0.214

)(0.441

)(0.860

)(0.601

)(0.666)

(0.555)

Logem

ploym

ent

Shortrunchange

(SR

POST

-SR

PRE)

0.01

90.01

9-0.047

-0.050

-0.018

-0.025

-0.019

-0.019

(0.600

)(0.694

)(0.004)

(0.020)

(0.227

)(0.222

)(0.210)

(0.342)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.031

-0.084

-0.071

-0.064

-0.021

-0.032

-0.061

-0.104

(0.563

)(0.208

)(0.011)

(0.065)

(0.379

)(0.318

)(0.017)

(0.001)

Pre-existingtrend(SR

PRE-LR

PRE)

-0.063

-0.080

-0.022

-0.016

-0.013

-0.019

-0.021

-0.029

(0.041)

(0.041)

(0.128

)(0.441

)(0.280

)(0.260

)(0.136)

(0.130)

Sam

ple

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

Number

ofob

servations

18,683

13,947

56,753

42,209

84,020

62,917

62,795

47,249

Fixed

effects

Industry-size-age-year

51

Table 8 Panel C: Establishment exit propensity after the “going-private” decision: breakdownby acquirer typeThis table presents hazard models in Panel C-A, and exit dummy linear propensity models in Panel C-B, for each

acquirer type identified in Table 1, Panel B. In Column 1 of Panel B, the 3- (6-) year Exit dummy is a variable that

equals one if the plant exited (was sold or closed) in the next three (six) years and zero otherwise. In Column 2

of Panel B, the 3- (6-) year Sold dummy is a variable that equals one if the plant was sold in the next three (six)

years and zero otherwise. In Column 3 of Panel B, the 3- (6-) year Closed dummy is a variable that equals one if

the plant was shut down in the next three (six) years and zero otherwise. (These variables are undefined (missing)

for a plant for time periods after it is shut down). For both panels, for each establishment in the going private

sample, we include two establishments that are closest in size (employment) to the going private establishment

from within the same 3-digit SIC industry and age quartile as controls. In both Panel A and Panel B, the analysis

is done using time-invariant explanatory variables, so the data has one observation for each of the gone private and

control establishments. The data used to construct this sample is taken from the Longitudinal Business Database

(LBD). P-values based on standard errors clustering by control group cells are in parentheses.

Panel C - A: Exponential Exit Sold Closed

Hazard model 1 2 3

Management acquirers -0.223 0.094 -0.258

(0.000) (0.000) (0.000)

Management acquirers: hazard ratio 0.800 1.099 0.773

Operating acquirers 0.185 0.227 0.122

(0.000) (0.000) (0.000)

Operating acquirers: hazard ratio 1.203 1.255 1.130

Private equity acquirers 0.285 0.501 0.092

(0.000) (0.000) (0.000)

Private Equity acquirers: hazard ratio 1.330 1.650 1.096

Control group Industry-size-age matched

Other controls Employment, Age Employment, Age Employment, Age

Fixed Effects Industry, Year Industry, Year Industry, Year

Observations 203,311 183,329 203,311

Panel C - B: OLS using 3-year outcome dummy 6-year outcome dummy

Cell-Year fixed effects Exit Sold Closed Exit Sold Closed

1 2 3 1 2 3

Management acquirers -0.009 0.024 -0.033 0.030 0.064 -0.033

(0.072) (0.000) (0.000) (0.000) (0.000) (0.000)

Fixed effects Industry-size-age matched cell - Year Industry-size-age matched cell - Year

Number of Observations 52,256 52,256 52,256 45,749 45,749 45,749

Operating acquirers 0.019 0.003 0.016 0.054 0.037 0.017

(0.000) (0.134) (0.000) (0.000) (0.000) (0.000)

Fixed effects Industry-size-age matched cell - Year Industry-size-age matched cell - Year

Number of Observations 116,806 116,806 116,806 100,754 100,754 100,754

Private equity acquirers 0.081 0.053 0.027 0.066 0.072 -0.006

(0.000) (0.000) (0.000) (0.000) (0.000) (0.230)

Fixed effects Industry-size-age matched cell - Year Industry-size-age matched cell - Year

Number of Observations 64,639 64,639 64,639 55,051 55,051 55,051

52

Table

8Panel

D:Exitafter

“going-priva

te”andproductivity:

Tests

fordifferen

tialtargetingbyacq

uirer

type

Thistable

presents

OLSregression

sof

exitdummyvariables.

InColumn1theExitdummyisavariable

that

equalson

eiftheplantexited

(was

sold

orclosed)in

thenextthreeyearsandzero

otherwise.

InColumn2,

Solddummyisavariable

that

equalson

eiftheplantwas

sold

inthenextthree(six)yearsandzero

otherwise.

InColumn3theCloseddummyis

avariable

that

equalson

eiftheplantwas

shutdow

n

inthenextthreeyearsandzero

otherwise.

(Thesevariablesareundefined

(missing)

foraplantfortimeperiodsafteritisshutdow

n).

For

bothpanels,

foreach

establishmentin

thego

ingprivatesample,weincludetwoestablishments

that

areclosestin

size

(employm

ent)

tothe

goingprivateestablishmentfrom

within

thesame3-digitSIC

industry

andagequartile

ascontrols.

Theanalysisisdon

eusingtime-invariant

explanatoryvariables,

sothedatahas

oneob

servationforeach

ofthego

neprivateandcontrol

establishments.Thedatausedto

construct

this

sample

istakenfrom

theCensusof

Manufactures(C

MF),

theAnnual

Survey

ofManufactures(A

SM)andtheLon

gitudinal

Business

Database(LBD).P-values

based

onstandarderrors

clustered

byindustry-size-agegrou

psarein

parentheses.

Exit

Sold

Closed

Exit

Sold

Closed

Exit

Sold

Closed

12

31

23

12

3

ProductivityMeasure

=Lab

orProductivity

OLSFETFP

Blundell-Bon

dTFP

Operatingacquirers

0.53

30.29

50.23

80.38

40.31

0.07

40.285

0.284

0.001

(0.000

)(0.005

)(0.016

)(0.016

)(0.017

)(0.527

)(0.019

)(0.004)

(0.992)

Managem

entacquirers

-0.082

-0.172

0.09

0.05

2-0.044

0.09

60.166

0.05

0.115

(0.698

)(0.264

)(0.581

)(0.802

)(0.765

)(0.563

)(0.362

)(0.696)

(0.434)

Private

equityacquirers

0.46

20.32

30.14

0.50

30.32

10.18

20.49

0.355

0.135

(0.020

)(0.023

)(0.363

)(0.013

)(0.032

)(0.258

)(0.003

)(0.004)

(0.284)

ProductivityMeasure

-0.023

-0.001

-0.022

-0.026

-0.007

-0.019

-0.026

-0.002

-0.024

(0.125

)(0.928

)(0.067

)(0.279

)(0.697

)(0.291

)(0.317

)(0.920)

(0.207)

OperatingXProductivityMeasure

-0.072

-0.039

-0.033

-0.064

-0.061

-0.004

-0.055

-0.078

0.024

(0.003)

(0.040)

(0.067)

(0.137

)(0.081)

(0.897

)(0.242

)(0.040)

(0.517)

Managem

entX

ProductivityMeasure

0.02

10.03

9-0.018

-0.007

0.01

9-0.026

-0.055

-0.009

-0.046

(0.609

)(0.194

)(0.574

)(0.901

)(0.635

)(0.555

)(0.439

)(0.854)

(0.420)

Private

EquityXProductivityMeasure

-0.059

-0.041

-0.018

-0.095

-0.056

-0.038

-0.134

-0.096

-0.038

(0.111

)(0.129

)(0.520

)(0.079)

(0.162

)(0.366

)(0.039)

(0.041)

(0.429)

Observations

9,64

69,64

69,64

69,64

69,64

69,64

69,646

9,646

9,646

Fixed

Effects

Industry-age-sizematched

cell-year

53

Figure 1: Before-after productivity trends in event timeThis figure displays the evolution of productivity measures in event time for the going private sample. Theconfidence intervals are based on standard errors clustered by plant.

3.1

3.2

3.3

3.4

3.5

−6 −4 −2 0 2 4 6

Labor Productivity: Before−After changes3.

553.

63.

653.

73.

75

−6 −4 −2 0 2 4 6

OLS TFP: Before−After changes

2.35

2.4

2.45

2.5

−6 −4 −2 0 2 4 6

Blundell−Bond TFP: Before−After changes

(5% confidence intervals)

Years from going−private

Productivity Trends

54

Figure 2: Difference-in-differences productivity trends in event time

This figure displays the evolution of productivity measures in event time for the going private sample,relative to a industry-age-initial size matched control group.The confidence intervals are based on standarderrors clustered by control group cells.

0.0

5.1

.15

.2

−6 −4 −2 0 2 4 6

Labor Productivity: DID changes

0.0

2.0

4.0

6.0

8

−6 −4 −2 0 2 4 6

OLS TFP: DID changes

−.0

4−

.02

0.0

2.0

4.0

6

−6 −4 −2 0 2 4 6

Blundell−Bond TFP: DID changes

(5% confidence intervals)

Years from going−private

Productivity trends

55

Figure 3: Before-after and difference-in-differences (DID) trends for capital and employmentin event timeThis figure displays the evolution of capital and employment measures in event time for the going privatesample. The before-after figures use only the going private sample, and the DID figures show trendsrelative to an industry-age-initial size matched control group. The confidence intervals are based onstandard errors clustered by plant in the Before-After figures, and by control group cells in the DID figures.

8.6

8.65

8.7

8.75

8.8

−6 −4 −2 0 2 4 6

Log deflated capital: Before−After changes

4.9

4.95

55.

05

−6 −4 −2 0 2 4 6

Log employment: Before−After changes

−.1

0.1

.2.3

−6 −4 −2 0 2 4 6

Log deflated capital: DID changes

−.0

50

.05

.1

−6 −4 −2 0 2 4 6

Log employment: DID changes

(5% confidence intervals)

Years from going−private

Capital and employment trends

56

SUPPLEMENTARY APPENDIX

TABLES

57

Table A.1: Changes in capital and employment after “going-private” : Before-After andDifference-in-Differences specifications, excluding sold plants

This table presents before-after and difference-in-differences regression results for capital and employment. The

data used to construct this sample excludes all sold plants (i.e., those that underwent a change in ownership

after the going private date) in both the treatment and control samples. In DID1, for each establishment in the

going private sample, we include upto eight establishments (based on data availability) that are closest in size

(employment) to the going private establishment from within the same 3-digit SIC industry, and belonging to the

same age quartile as controls. In DID2, for each establishment in the going private sample, we include upto eight

establishments (based on data availability) that are closest in past TFP to the going private establishment from

within the same 3-digit SIC industry, and belonging to the same age quartile as controls. In DID3, we include upto

eight establishments within the same 3-digit SIC industry closest in propensity to go private (but whose owner

firms did not go private), as control establishments. The data used to construct this sample is taken from the

Census of Manufactures (CMF) and the Annual Survey of Manufactures (ASM). Dummy variables LR PRE equals

1 for years -6 to -4 from going private and zero otherwise, SR PRE equals 1 for years -3 to -1 from going private

and zero otherwise, SR POST equals 1 for years 0 to 3 from going private and zero otherwise, and LR POST

equals 1 for years 4 to 6 from going private and zero otherwise. P-values based on standard errors clustered

by plant establishments (before-after), industry-size-age cells (DID1), industry-past TFP-age cells (DID2) and

industry-propensity cells (DID3) are in parentheses.

Log deflated capital Log employment

Before-After DID1 DID2 DID3 Before-After DID1 DID2 DID3

1a 1b 1c 1d 2a 2b 2c 2d

LR PRE 8.599 0.212 0.402 -0.232 4.984 0.051 0.213 -0.176

(0.000) (0.000) (0.000) (0.117) (0.000) (0.000) (0.000) (0.078)

SR PRE 8.6143 0.223 0.721 -0.106 4.9374 0.025 0.451 -0.071

(0.000) (0.000) (0.000) (0.408) (0.000) (0.001) (0.000) (0.441)

SR POST 8.645 0.1150 0.4900 -0.1510 4.881 0.0003 0.3020 -0.0530

(0.000) (0.000) (0.000) (0.301) (0.000) (0.981) (0.000) (0.614)

LR POST 8.6899 0.057 0.303 -0.249 4.853 -0.029 0.144 -0.089

(0.000) (0.112) (0.000) (0.160) (0.000) (0.136) (0.001) (0.489)

CHANGES RELATIVE TO SR PRE

SR POST - SR PRE 0.0307 -0.1080 -0.2310 -0.0450 -0.0564 -0.0245 -0.1490 0.0178

(0.001) (0.000) (0.000) (0.681) (0.000) (0.056) (0.000) (0.831)

LR POST - SR PRE 0.0756 -0.1656 -0.4180 -0.1430 -0.0844 -0.053 -0.3070 -0.0184

(0.000) (0.000) (0.000) (0.386) (0.000) (0.008) (0.000) (0.881)

TEST FOR PRE-EXISTING TREND

SR PRE - LR PRE 0.0153 0.011 0.319 0.126 -0.0466 -0.026 0.238 0.105

(0.157) (0.611) 0.000 (0.269) (0.000) (0.022) (0.000) (0.180)

Fixed effects Plant Cell-year Cell-year Cell-year Plant Cell-year Cell-year Cell-year

Observations 20,788 139,826 136,218 36,659 20,788 139,826 136,218 36,659

58

Table

A.2:Changes

inproductivitymea

sures:

Differen

ce-in-D

ifferen

cesbyacq

uirer

type,

matchingonlagged

TFP

Thistable

presents

testsof

differencesin

productivitybased

onseparateregression

sforeach

acquirer

typeidentified

inTable

1,Panel

B.For

each

establishmentin

thego

ingprivatesample,weincludeupto

eigh

testablishments

(based

ondataavailability)that

areclosestin

lagged

(Blundell-Bon

d)TFP

tothego

ingprivateestablishmentfrom

within

thesame3-digitSIC

industry,andbelon

gingto

thesameagequartile

ascontrols.

Column1includes

allestablishments;in

Column2,

thesample

forboththego

ingprivateandmatched

control

establishments

excludes

allsold

plants

(i.e.,

thosethat

underwentachange

inow

nership

afterthego

ingprivatedate).Thedatausedto

construct

this

sample

istakenfrom

theCensusof

Manufactures(C

MF)andtheAnnual

Survey

ofManufactures(A

SM).

DummyvariablesLR

PREequals

1foryears-6

to-4

from

delistingandzero

otherwise,

SR

PREisadummyvariable

that

equals1foryears-3

to-1

from

delistingandzero

otherwise,

SR

POST

equals1foryears0to

3from

delistingandzero

otherwise,

andLR

POST

equals1foryears4to

6from

delistingand

zero

otherwise.

Standarderrors

clustered

bycontrol

grou

pcells

areusedto

assess

theP-values

(inparentheses)forthedifferenttests.

Unclassified

Managem

ent

Operating

Priva

teEquity

12

12

12

12

Laborproductivity

Shortrunchange

(SR

POST

-SR

PRE)

0.06

80.06

40.02

20.01

80.01

1-0.002

0.009

0.004

(0.198

)(0.353

)(0.372

)(0.581

)(0.637

)(0.951

)(0.700)

(0.894)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.044

-0.018

-0.017

-0.042

0.03

70.04

00.007

0.007

(0.597

)(0.875

)(0.652

)(0.408

)(0.234

)(0.327

)(0.841)

(0.895)

Pre-existingtrend(SR

PRE-LR

PRE)

0.04

20.05

00.047

0.075

0.01

60.03

20.033

0.068

(0.360

)(0.424

)(0.066)

(0.035)

(0.450

)(0.237

)(0.202)

(0.045)

OLSTFP

Shortrunchange

(SR

POST

-SR

PRE)

-0.037

-0.039

-0.017

-0.024

-0.015

-0.020

-0.010

-0.015

(0.206

)(0.341

)(0.180

)(0.173

)(0.184

)(0.200

)(0.378)

(0.380)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.146

-0.167

-0.042

-0.037

0.00

40.00

7-0.030

-0.041

(0.001)

(0.006)

(0.034)

(0.208

)(0.812

)(0.748

)(0.100)

(0.127)

Pre-existingtrend(SR

PRE-LR

PRE)

0.055

0.065

0.027

0.02

70.033

0.044

0.034

0.044

(0.040)

(0.079)

(0.023)

(0.098

)(0.001)

(0.000)

(0.004)

(0.006)

BlundellBondTFP

Shortrunchange

(SR

POST

-SR

PRE)

-0.019

-0.007

0.00

50.00

2-0.007

-0.012

-0.001

-0.005

(0.476

)(0.832

)(0.661

)(0.894

)(0.504

)(0.410

)(0.902)

(0.760)

Lon

grunchange

(LR

POST

-SR

PRE)

-0.132

-0.137

-0.005

-0.014

0.01

20.01

8-0.004

-0.004

(0.011)

(0.067)

(0.778

)(0.655

)(0.429

)(0.406

)(0.831)

(0.864)

Pre-existingtrend(SR

PRE-LR

PRE)

0.055

0.02

80.02

70.00

30.033

0.01

00.034

0.011

(0.040)

(0.338

)(0.023

)(0.804

)(0.001)

(0.380

)(0.004)

(0.406)

Fixed

effects

Industry-PastTFP-age-year

Sam

ple

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

All

Excludes

sold

Number

ofob

servations

19,926

15,074

52,863

40,234

79,773

60,773

60,194

45,769

59

Table A.3: Changes in establishment profit measures around the “going-private” decision :Before-After and Difference-in-Differences SpecificationsThis table presents regression results for two profit-related measures. The first is gross profits, defined as sales

less sum of (materials cost, energy costs, blue collar wage bill and white collar wage bill). The second is the ratio

of gross profits to sales. Both measures are winsorized by 2% on both tails to minimize effects of outliers. In

DID1, for each establishment in the going private sample, we include upto eight establishments (based on data

availability) that are closest in size (employment) to the going private establishment from within the same 3-digit

SIC industry, and belonging to the same age quartile as controls. In DID2, we include upto eight establishments

within the same 3-digit SIC industry closest in propensity to go private (but whose owner firms did not go private),

as control establishments. The data used to construct this sample is taken from the Census of Manufactures (CMF)

and the Annual Survey of Manufactures (ASM). Dummy variables LR PRE equals 1 for years -6 to -4 from going

private and zero otherwise, SR PRE equals 1 for years -3 to -1 from going private and zero otherwise, SR POST

equals 1 for years 0 to 3 from going private and zero otherwise, and LR POST equals 1 for years 4 to 6 from going

private and zero otherwise. P-values based on standard errors clustered by plant establishments (before-after),

industry-size-age cells (DID1) and industry-propensity cells (DID2) are in parentheses.

Gross Profit Return on sales

Before-After DID1 DID2 Before-After DID1 DID2

1a 1b 1c 1a 1b 1c

LR PRE 9490.0 34.6 -1160 24.630 0.744 0.732

(0.000) (0.909) (0.277) (0.000) (0.056) (0.446)

SR PRE 11274.0 195.5 -873.8 26.001 0.874 1.165

(0.000) (0.510) (0.358) (0.000) (0.021) (0.165)

SR POST 12869.0 301.0 -1332 27.195 1.223 0.547

(0.000) (0.407) (0.285) (0.000) (0.002) (0.581)

LR POST 14270.0 523.7 -627.4 25.999 0.898 1.620

(0.000) (0.324) (0.706) (0.000) (0.090) (0.223)

CHANGES RELATIVE TO SR PRE

SR POST - SR PRE 1595.0 105.5 -458.2 1.194 0.349 -0.618

(0.000) (0.712) (0.651) (0.000) (0.377) (0.538)

LR POST - SR PRE 2996.0 328.2 246.4 -0.002 0.024 0.455

(0.000) (0.490) (0.875) (0.996) (0.967) (0.749)

TEST FOR PRE-EXISTING TREND

SR PRE - LR PRE 1784.0 160.9 286.2 1.371 0.130 0.433

(0.000) (0.537) (0.743) (0.000) (0.734) (0.664)

Fixed effects Plant Cell-year Cell-year Plant Cell-year Cell-year

Number of observations 28,518 185,909 55,358 28,518 185,909 55,358

60