Do Going-Private Transactions Affect PlantEfficiency and Investment?
Sreedhar BharathW. P. Carey School of Business, Arizona State University
Amy DittmarRoss School of Business, University of Michigan
Jagadeesh SivadasanRoss School of Business, University of Michigan
We examine whether constraints on public firms affect firms’ efficiency by testing if goingprivate improves plant-level productivity relative to peer control groups. We find that,despite increases in productivity after going private, there is little evidence of efficiencygains relative to peer groups of plants constructed to control for industry, age, size, pastproductivity, and the endogeneity of the going-private decision. Going-private firms doextensively restructure their portfolio of plants, selling and closing plants more quicklythan others. Our findings cast doubt on the view that public markets cause listed firms tooperate plants less efficiently due to overinvestment but indicate that going private increasesrestructuring activity. (JEL G34, G14, D24, D22)
Are private firms more efficient than public firms? Jensen (1986) suggests thatgoing private could result in efficiency gains by aligning managers’ incentiveswith shareholders’ and providing better monitoring. The dispersed ownershipstructure of public firms may lead firms to overinvest and allocate resourcesinefficiently (Jensen 1986; Jensen and Meckling 1976). Porter (1992) arguesthat the U.S. system advances the goals of shareholders interested in near-termappreciation of their shares even at the expense of long-term performance ofAmerican companies.
In this paper, we examine a broad dataset of going-private transactions,including those taken private by private equity, management, and private
The research in this document was conducted while the authors were Census Bureau research associates at theMichigan 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 beenscreened to ensure that no confidential data are revealed. We thank Clint Carter and Arnold Reznik for promptprocessing of our disclosure and data access requests, Natarajan Balasubramanian for advice and help withthe data, and Xiaoyang Li for research assistance. We also thank the editor and the anonymous referees. Allremaining errors are our own. Supplementary data can be found on the Review of Financial Studies web site.Send correspondence to Amy Dittmar, Ross School of Business, University of Michigan, 701 Tappan, Ann Arbor,MI 48109-1234; telephone: (734) 764-3108. E-mail: [email protected].
operating firms between 1981 and 2005. We link data on going-privatetransactions to rich plant-level U.S. Bureau of Census microdata to examinehow going private affects plant-level productivity, investment, and exit (saleand closure). Prior to going private, in some specifications, we find plantsof public firms that go private are more productive than their matched peers.However, we find no evidence that going private causes plants to improve theirproductivity relative to their matched peers. Specifically, while we find within-plant increases in measures of productivity after going private, there is littleevidence of efficiency gains after going private relative to a sample matchedby industry, size, and age. Further, our productivity results hold excluding allplants that underwent a change in ownership after going private, alleviatingthe potential concern that control plants may undergo improvements throughownership changes.
To address potential endogeneity concerns, we create additional matchedsamples using past plant productivity and the propensity to go private. Tomeasure the propensity to go private, we use information for all the firms at thetime of their initial public offering (IPO). In doing so, we build on the workof Bharath and Dittmar (2010), who show that firm characteristics at the timeof the IPO predict the ultimate decision to go private with a 71% accuracyrate. We use their model to determine the propensity of a firm to go private.Both the propensity and past productivity matched results continue to showthat plants of firms that go private do not improve their productivity relativeto these samples. Thus, our findings suggest that the operational efficiency ofestablishments that went private is not differentially enhanced even six yearsafter going private. While public, these same establishments have either higherproductivity or no difference relative to control establishments. These resultsserve to cast doubts on the popular view that going private produces productivitygains in plant-level operations by alleviating agency issues or overinvestmentproblems (Jensen 1986).
Next, we examine investment decisions and establishment exits. Specifically,we examine establishment-level capital stock, employment, and plant exitsthrough sales and closures. We find that firms shrink capital stock andemployment in the six years after going private, consistent with Kaplan’s(1989a) findings for management buyouts. Specifically, going-private firmsdecrease capital by 15.1% and employment by 3.3% relative to a size,industry, and age control group. Despite these decreases in inputs, there are noproductivity gains because output also falls commensurately for these firms.We further find that going-private firms exit plants more quickly (15.3% higherhazard rate) than this same matched control group, particularly in the threeyears after going private. The higher exit is driven mainly by greater sales(33% higher hazard rate) rather than by closures (6.1% higher hazard rate).
We finally test whether going-private firms target lower-performing plantsfor sales and closures after delisting. We find that going-private firms are morelikely to exit by selling plants that have lower productivity. The results for plant
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closures are similar, but the estimates are not statistically significant. Thus, thegoing-private firms appear to take actions that improve the productivity of theirportfolio of plants, but they achieve this through selling low-productivity plantsrather than through productivity improvements of individual plants. Takentogether, these results suggest that: (i) agency problems and other constraintssuch as short-termism associated with public capital markets do not affect oper-ational plant efficiency; (ii) investors in going-private transactions gain valuenot by improving productivity within target plants, but by identifying produc-tive plants, selling and closing (less-productive) plants, and reducing capital.
To explore potential alternative explanations of our results, we examinedifferences by types of acquirers involved in going-private transactions.Specifically, we classify transactions into three subgroups—private equityfirms, management, and private operating firms—by collecting data on theparties that drive these transactions. We then compare changes in these groupsrelative to the matched control group used in earlier analysis. One explanationfor the findings in this paper is that the high leverage used or the time horizonfor recouping investment in going-private transactions could lead to short-termism and suboptimal decision-making after delisting, as well as pressureto downsize plants. Because higher leverage and the short investment horizonare more likely to affect management buyouts and private equity takeovers,examining the outcomes for operating acquisitions allows us to consider asubsample where high leverage is less of a concern.
In most specifications, regardless of the mechanism used to go private, thereis no improvement in productivity relative to the matched control group. Thus,operating firm takeovers, which constitute about 45% of our sample and willbe less affected by change in leverage, do not improve efficiency after goingprivate. We also find that operating firms have 8.3% to 11.6% declines in capitalin the six years after going private, but smaller and not statistically significantdeclines in employment. Further, operating firms show a significantly higherpropensity to both sell and close establishments relative to matched controls;in fact, operating acquirers are more likely to close a plant relative to matchedcontrols than either private equity or management acquirers in the six years aftergoing private. The propensity to sell is the highest for private equity acquirersrelative to the control group, with a hazard rate more than twice that of operatingacquirers and six times that of management acquirers. These qualitativelysimilar results for the operating firm sample and the overall sample suggestthat leverage or investor short-termism is unlikely to be the main explanationfor the baseline results.
We perform a number of checks and additional analysis to determine therobustness of our results. We analyze outcomes at the acquiring firm toinvestigate if changes there offset results for the going-private establishments.We find no relative increases in capital, employment, or productivity measuresat acquiring firm establishments, and also no increase in new establishmentopenings at acquiring firms. In addition to this, we use profitability (instead of
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productivity) measures as alternative performance measures, explore changesin outcomes over different time periods, and check robustness to usingalternative specifications.
Our paper contributes to the long literature that examines productivitychanges around corporate events. Maksimovic and Phillips (2001) andMaksimovic, Phillips, and Prabhala (2011) examine productivity changes andpurchase decisions by acquirers after mergers and acquisitions to understandhow firms redraw their boundaries and evaluate the efficiency of resourcereallocation. Maksimovic and Phillips (2002, 2008) find that plants acquired byconglomerate firms increase in productivity and conclude that organizationalforms’comparative advantages differ across industry conditions. Our paper hasa similar approach but focuses on public to private transactions.
Lichtenberg and Siegel (1991) find that productivity increases after aleveraged buyout (LBO) using a sample of 131 firms that conducted an LBOin the mid-1980s. Kaplan (1989a, 1989b; 1991) examines the benefits of goingprivate using a sample of LBOs and highlights the importance of tax andincentive improvements due to the high leverage in these transactions. Ourpaper expands on these studies by examining a longer timeframe and a sampleof firms that in addition to LBOs and MBOs includes a large number of privateoperating firms buying public firms and taking them private.
Davis et al. (2008a, 2008b) study changes after a private equity deal for theuniverse of private-equity firm deals, the vast majority of which are private-to-private transactions. They find evidence of increases in productivity, jobcreation as well as job destruction, and establishment entry and exit after atransaction. In contrast, our study focuses on public-to-private transactionsof manufacturing firms, including those acquired by private operating firms,and find no gain or loss in productivity after going private relative to matchedcompetitors, though we do find increased restructuring activity. Based on public13E-3 filings with the Securities and Exchange Commision (SEC), public-to-private deals account for 157 out of more than 5,000 deals done by private-equity firms from 1980 to 2005, thus explaining the differences in our findings.
Our study also differs from that of Asker, Farre-Mensa, and Ljungqvist(2012), who compare the investment of a set of private firms with a matchedsample of small listed firms and find that their sample of private firms investsmore than the small listed firms. We focus on the change in productivity andinvestment in labor and capital of firms before and after going private, relative toa matched sample and employing the Census data. Thus, we are able to examinea large set of firms, which are economically more important in a value-weightedsense, and address differences between the comparison samples by evaluatingthe effect of going private on their productivity.
In addition to providing evidence that plant-level productivity is notadversely affected by the overinvestment problem due to agency conflict inlisted firms (Jensen 1986; Jensen and Meckling 1976), this paper also shedslight on the potential for capital market myopia, as described in Stein (1989) and
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supported by evidence in Graham, Harvey, and Rajgopal (2005) and Bhojrajet al. (2009). If market myopia leads to underinvestment, we would expect arelative increase in capital stock and employment, and a greater patience toexit underperforming plants after going private. The myopia hypothesis wouldalso predict that there would be productivity gains relative to public firms oncethe constraint is removed by going private. We find no evidence that this is thecase; thus, our evidence is largely inconsistent with public firms being moresubject to myopia.
1. Data and Productivity Measures
1.1 Data sources and descriptionThe 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 AnnualSurvey of Manufactures (ASM) for remaining between-Census years from1978 to 2004. These Census data provide detailed information on individualestablishments (i.e., plants) of both public and private firms. These data havebeen used in previous studies, particularly to study the effects of mergersand acquisitions on productivity, discussed in the introduction. We alsouse the Longitudinal Business Database (LBD) to obtain identifiers to linkestablishments over time, and for data used in exit analysis. Technical detailson the cleaning of the data, as well as detailed definitions of the key variablesused in the study, are provided in the Data Appendix. Detailed descriptionsof the productivity variables are provided in the next section (with additionaldetails provided in the Data Appendix).
To analyze the effect of going private on firm productivity and other outcomemeasures, we use a comprehensive sample of firms that went private as detailedin Bharath and Dittmar (2010). Bharath and Dittmar (2010) use all forms of13E-3 filings to identify going-private transactions and require that firms are nolonger registered or traded (even over the counter). They also supplement theirsample with data from Lehn and Poulsen (1989) to ensure a complete samplein the early periods.
We then match these firms to Census databases using the Compustat-SSEL bridge available at the Census using six-digit CUSIP identifers forthe period 1981 to 2005 to identify all establishments owned by the sampleof going-private firms. In the baseline analysis, we create a control samplefor each establishment in the going-private sample (hereafter “going-privateestablishment”), by including up to eight establishments that are closest in size(measured using employment) to the going-private establishment in the going-private year, from within the same three-digit SIC industry, and belonging tothe same age quartile. We use this matched sample throughout but also provideevidence using alternative matched samples by industry, past productivity, andage as well as one based on propensity score matching on the probability a firmwill go private.
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Table 1, Panel ASample characteristics, by years, around the “going-private” decision
Years Number Number Total Numberfrom Total of going- of matched number of going- Numbergoing number of private control of private of controlprivate establishments establishments establishments firms firms firms
The data used to construct this sample are taken from the Census of Manufactures (CMF) and theAnnual Survey ofManufactures (ASM). For each establishment in the going-private sample, we include up to eight establishments(based on data availability) that are closest in size (employment) to the going-private establishment from withinthe same three-digit SIC industry, and belonging to the same age quartile as controls.
Table 1, Panel BSample characteristics: Breakdown by acquirer type
Number NumberTotal Number of of matched Total of going- Number
Acquirer number of going-private control number of private of controltype establishments establishments establishments firms firms firms
We 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 newspaperreports from Factiva. The residual category is unclassified. The category types are nonexclusive, so that somedeals are classified under more than one type; hence the net total number is not the sum of the numbers in thesubcategories. The data used to construct this sample are taken from the Census of Manufactures (CMF) andthe 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-privateestablishment from within the same three-digit SIC industry, and belonging to the same age quartile as controls.
Panel A of Table 1 presents the summary statistics on the number of firmsand establishments in event time for a period of thirteen years, with year 0 beingthe date of going private. Overall, we have 28,518 going-private establishment-year and 157,391 control firm establishment-year observations, representing5,676 going-private and 90,009 control sample firm years in the sample. All ofour analysis examines outcomes at the establishment level.1
1 Because of attrition (due to closure or due to not yet being born), the number of establishments is lower in yearsbefore and after year –1. The first step of the data setup process identifies a baseline sample of going-private
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Table 1, Panel CCharacteristics of going-private (target) establishments: Breakdown by acquirer type
Going- Unclassified Operating PrivateCharacteristics private sample equity Management(based on Year –1 N =7,766 N =721 N =3,524 N =2,734 N =2,373to Year –3) Mean Mean Mean Mean Mean
This table presents sample characteristics of the going-private establishments. The figures in the last three rowsare demeaned using the three-digit SIC industry mean for the sample.
Table 1, Panel DCharacteristics of acquirer establishments: Breakdown by acquirer type
This table presents sample characteristics of the acquirer establishments. Because the acquirer for managementtakeovers 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.) The figures in the last threerows are demeaned using the three-digit SIC industry mean for the sample.
establishments and matched controls in year –1. Thus, some going-private establishments in year –1 were boughtfrom other firms before year –1 or sold to other firms after year –1, and the number of going-private firms islarger before and after year –1. For the control firms, attrition in number of establishments more than offsetsincreases due to sales, and so the number of firms is largest in year –1.
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While the establishment level changes provide a detailed and disaggregatedpicture of the effects of going private, aggregating to the firm level is difficulthere because the firm identifier for the establishments of the acquired firmwill 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 tofocus on establishments. First, many of the firms have multiple establishmentsoperating in multiple industries; thus, forming a suitable control group for afirm that matches the firm’s industry composition is more difficult. Second, oneaspect of firm behavior we specifically want to look at is the decision to sellor shut down particular establishments (see Section 3.2). The establishment-level analysis could mask improvements that occur due to selective closure ofinefficient establishments; we address this separately in Section 3.2.3.
Panel B of Table 1 breaks down the number of firms and establishmentsby type of acquirer — that is, the type of firm that took the firm private. Ofthe 406 (2,701) going-private firms (establishments), 112 (819) were takenprivate by management, 115 (958) by a private equity firm, and 188 (1,207) bya 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 thetransactions are classified under multiple groups, so there are a small numberof overlaps in these subgroups. For 48 (259) firms (establishments), we wereunable to determine the acquirer type.
Panels C and D of Table 1 provide summary statistics for the going-privatefirm (target) as well as the acquirer, respectively. Note that the acquirer is thesame as the target when it is management that takes the firm private. In bothPanels C and D, the labor productivity of the plants for both the target andthe acquirer for all classified groups is larger than the industry average. Bothtotal factor productivity (TFP) measures are systematically higher than theindustry mean for the acquirers (Panel D), but not for the target (Panel C), asthe Blundell-Bond TFP measure is negative for all groups except the targetsof private equity takeovers. Comparing these panels, the going-private samplehas 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 thosethat are unclassified.
1.2 Key productivity measuresIn this section, we discuss in detail the three alternative measures used inour analysis of establishment-level productivity, as well as the variables andmethodology used in their definition.
• Labor productivity: Labor productivity is defined as log real valueof shipments divided by employment. Value of shipments is simply the
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sales value deflated using four-digit SIC industry-specific output deflators(obtained from Becker and Gray’s 2009 NBER-CES manufacturingindustry database). Employment is the total number of employeesreported in the ASM-CMF database.
While labor productivity adjusts for increase in labor inputs, TFP measuresadjust for changes in all inputs. A standard measure of TFP is the residual ina Cobb-Douglas production function: yit =βmmit +βkkit +βeeit +βnnit +βllit +TFPit , where yit is the log of real value of shipments of establishment i inyear t , m is log real materials, k is log of real depreciated capital stock, e islog of real depreciated energy costs, n is log of white-collar (non-production)employment, and l is log of blue-collar (production) employment. The residualTFP is typically recovered using a regression of real output on inputs; however,this regression suffers from endogeneity as inputs are likely to be chosen basedon TFP (Marschak and Andrews 1944). We use two approaches to addressendogeneity concerns, as described below.2
• OLS-FE TFP measure: The main source of endogeneity in panelcontext is likely to due to fixed TFP heterogeneity across firms, arisingfrom unobserved differences in factors such as labor quality, locationaladvantages, and entrepreneurial ability. A solution is to use panel datatransformations with fixed effects, which control for any factors that donot change over time. The OLS-FE productivity measure is defined asthe residual from an ordinary least squares (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-collaremployment, and log real capital.
• Blundell-Bond system-GMM TFP measure: While the use of fixedeffects to solve the endogeneity problem is simple and appealing,in practice the approach often yields unrealistically low or evennegative coefficients for capital, because fixed effects increase the noisecomponent of the capital measure.3 Further, it is plausible that someproductivity shocks are anticipated, so that inputs respond to changes inproductivity as well. An alternative solution that addresses these issues isthe “system” generalized method of moments (GMM) approach, whichuses both first differences and levels (Blundell and Bond 2000), where
2 In results available in the Internet Appendix, we verified robustness to using three additional productivitymeasures: Solow residual TFP measure, Levinsohn and Petrin TFP measure, and Translog TFP measure. Forbrevity, we do not present these in the paper but note the results are similar throughout. The results are availablein the Internet Appendix Tables A.1 and A.2, which correspond to Tables 5 and 8 in the paper, respectively.
3 Intuitively, capital often has lagged effects, and investment could sometimes have short-run disruptive effects. Sochanges in capital often have low or negative correlation with contemporaneous changes in output. For example,in the period following installation of new computer systems, measured capital goes up, but output could fallwhile bugs are ironed out or workers learn the new system, leading to a negative correlation between changes incapital and changes in output. This issue is discussed in detail in Griliches and Mairesse (1995).
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lagged first differences are used as instruments for equations in levels, inaddition to lagged levels as instruments for equations in first differences.A brief description of the Blundell-Bond (2000) procedure used by us isprovided in the Data Appendix.
Because the Blundell-Bond approach addresses potentially importantendogeneity issues in the most systematic manner, we use this measure asour main baseline measure. We also report the other two measures becausethese are widely used in the productivity literature and because the Blundell-Bond coefficient estimates are sometimes sensitive to choice of instruments, sorobustness to these alternative measures would provide reassurance about theresilience of our results.
Conceptually, all three TFP measures can be thought of measures of outputconditioning on a weighted average index of inputs. However, when comparingthe magnitudes of labor productivity to TFP measures, it should be noted that ifa firm experiences larger increases in capital (or other inputs) relative to labor,the magnitude of the labor productivity measure could be significantly higherthan for TFP because labor productivity does not adjust for changes in capital.Specifically, the labor productivity measure is simply log of real output (sales)per employee. Also, as is well known in the literature, the OLS fixed effectproduction function often yields very low capital coefficient estimates, due tomeasurement error in capital (Griliches and Mairesse 1995).
In our analysis, as expected, we find that relative to the Blundell-Bondestimator, the coefficient on the employment variable is upward biased in thesimple OLS analysis, while the capital coefficient is downward biased whenusing OLS fixed effects.4 The Blundell-Bond approach utilizes equations inboth levels and differences, places greater weight on capital than the othermeasures, and yields more realistic capital coefficient estimates. Thus, theBlundell-Bond approach is our preferred measure for the interpretation of ourresults, though there are indeed reassurances about the robustness of the resultsif all measures agree in sign and significance.
2. Analysis of Productivity Changes
When examining productivity and other outcomes, we present two sets ofresults. The first set of “before-after” results summarizes what happened to thekey variables of interest within the establishments that belonged to firms thatwent private, compared to their levels prior to going private. The second set of“difference-in-differences” (DID) results presents the changes in the variables
4 However, despite differences in coefficient estimates, we find that the estimated TFP residuals are very highlycorrelated (correlation greater than 0.85) in our sample, so it is not surprising that in the analysis below wefind consistent results across alternative approaches. This consistency across alternative TFP estimators havebeen found by others using U.S. Census micro data, e.g., Greenstone, Hornbeck, and Moretti (2010). As in theliterature, we allow production to function coefficients to vary by industry (two-digit SIC codes).
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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 subsections.
2.1 The before-after methodologyTo examine before-after changes, we retain data for as many of the thirteenyears surrounding the event as available for each establishment belonging tothe firms that went private. These include up to six years of data before the yearof going private, the year that the firm went private, and up to six years afterthe firm went private.5
We then use simple regression approaches to summarize the before-afterchanges in two ways. First, we use the following regression specification:
where yit stands for the dependent variable (productivity or other measures),fi stands for plant fixed effects, and the four dummy variables are definedto capture four distinct time periods as follows: (i) the long run before goingprivate: LR_PRE is a dummy equal to one for the three-year period from sixto four years before going private and zero otherwise; (ii) the short run beforegoing private: SR_PRE is a dummy equal to one for the three-year period fromthree to one years before going private and zero otherwise; (iii) the short-runafter going private: SR_POST is a dummy equal to one for the four-year periodfrom zero to three years after going private and zero otherwise; and (iv) thelong-run after going private: LR_POST is a dummy equal to one for the three-year period from four to six years after going private and zero otherwise. Theterm eit stands for residual error.
The estimates of primary interest are not the levels of the dependent variables,but rather their changes over time.6 Specifically, we are interested in thefollowing changes:
(i) Short run after versus short run before going private (βSR_POST −βSR_PRE): This provides an estimate of the short-run changes aftergoing-private, relative to the period just before the going-private event.Thus, if the new owners take steps that have immediate effects on theperformance of the plant, this should be reflected in this estimate.
(ii) Long-run after versus short-run before going private (βLR_POST −βSR_PRE): This provides an estimate of long-run changes in the dependent
5 Note that there may be some establishments that were born less than six years before the firm went private, andsome establishments that exit less than six years after the going-private event.
6 The 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.
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Columns 1–3 present regression results for each of the productivity measures for the sample of establishmentsof firms that went private. The data used to construct this sample are taken from the Census of Manufactures(CMF) and the Annual Survey of Manufactures (ASM). Dummy variable LR_PRE equals 1 for years –6 to –4from going-private and 0 otherwise, SR_PRE equals 1 for years –3 to –1 from going-private and 0 otherwise,SR_POST equals 1 for years 0 to 3 from the going-private date and 0 otherwise, and LR_POST equals 1 for years4 to 6 from the going-private date and 0 otherwise. p-values based on standard errors clustered by establishmentare in parentheses. Columns 4–6 present difference-in-differences regression results for each of the differentproductivity measures. For each establishment in the going-private sample, we include up to eight establishments(based on data availability) that are closest in size (employment) to the going-private establishment from withinthe same three-digit SIC industry, and belonging to the same age quartile as controls. p-values based on standarderrors clustered by industry-size-age cells are in parentheses.
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, wemay obtain significant estimates here, but not in (i) above.
(iii) Test for prior trend (βSR_PRE −βLR_PRE): This provides an estimateof trends in the dependent variable prior to going private. If theestablishment was experiencing an increasing (decreasing) trend in thedependent variable, this would manifest as a positive (negative) estimatein this test. Thus, any changes we document in (i) or (ii) above shouldbe evaluated in the context of the preexisting trend captured by theestimate here.
The first three columns of Table 2 present the results of this analysis. PanelA includes all establishments with available data. Panel B restricts the sampleto only those establishments that were not later sold in years t +1 to t +6 toensure that the effects are due to going private and not due to a subsequent
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Table 2, Panel BChanges in productivity around the “going-private” decision: BA and DID specifications, excluding soldplants (from both going-private and control samples)
Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP
The data used to construct this sample exclude all sold plants (i.e., those that underwent a change in ownership afterthe going-private date) in both the treatment and control samples and are taken from the Census of Manufactures(CMF) and the Annual Survey of Manufactures (ASM). Columns 1–3 present regression results for each of theproductivity measures for the sample of establishments of firms that went private. Dummy variable LR_PREequals 1 for years –6 to –4 from the going-private date and 0 otherwise, SR_PRE equals 1 for years –3 to –1from the going-private date and 0 otherwise, SR_POST equals 1 for years 0 to 3 from the going-private date and0 otherwise, and LR_POST equals 1 for years 4 to 6 from the going-private date and 0 otherwise. p-values basedon standard errors clustered by establishment are in parentheses. Columns 4–6 present difference-in-differencesregression results for each of the different productivity measures. For each establishment in the going-privatesample, we include up to eight establishments (based on data availability) that are closest in size (employment)to the going-private establishment from within the same three-digit SIC industry, and belonging to the same agequartile as controls. p-values based on standard errors clustered by industry-size-age cells are in parentheses.
sale in either the going private or the control group. For all inferences, wecompute p-values based on standard errors clustered by establishments.7 Thefirst column regresses labor productivity, while columns 2 and 3 measure TFPaccording to the two methods described briefly in Section 1.2.
We first note that going-private firms have higher productivity based on allthree measures in both the short and long run both before and after the event, asevidenced by the positive and significant coefficient on (SR)LR_PRE(POST).Further, we find that there is a preexisting improving trend in all the productivityvariables between 3.4% and 10.5% (based on Table 2, Panel A) prior to thegoing-private decision, as evidenced by the positive and significant coefficienton short-run pre- versus long-run pre-comparisons.
7 The significance levels were similar when we clustered by firm.
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Examining the changes over time, we find that labor productivity, OLSTFP, and Blundell-Bond TFP increase both in the short run by 6.3%, 3.2%,and about 3.7%, respectively, and in the long run by 7.2%, 6.4%, and about5.1% respectively after going private, and these differences are statisticallysignificant. Because TFP is measured as a residual from a regression of realoutput on inputs, increases in TFP translate to increases in output holdinginputs fixed. Converting the three years means into annual rates, our numbersindicate the increase in TFP indicates approximately a 1% annual increase inreal output (net of increase in inputs) for the going-private establishments inthe post-going-private period; however, this is close to the per-year increase inthe pre-going-private period, so the regression results do not indicate a strongchange in trend.
The first three columns of Table 2, Panel B, present similar results. Asnoted before, in this panel, we exclude establishments that are subsequentlysold, resulting in approximately 8,000 fewer establishment-year observations.Similar to the full sample, the establishments exhibit a significant pre-periodtrend and a significant increase in productivity from before to both the short andlong run after going private. In fact, the economic significance of the results isconsiderably stronger in this subsample, with the productivity increase between7% and 19% in the short run and 10% to 21% in the long run. These results areconsistent with the acquiring firm selling establishments that do not experiencean increase in productivity. In Section 3.2.3, we will directly examine thecharacteristics of the establishments that are sold.
To allow for a more flexible examination of the year-by-year effects, weexamine a standard event study graph, by plotting coefficients on the indexdummies from the following regression specification.
yit =6∑
k=−6
βkDk +fi +eit (2)
where k indexes the years after the going-private event, and correspondinglyDk is a dummy variable equal to one for the year k after the going-privateevent. Negative values of k correspond to years before going private. All othervariables are as in Equation (1) above.
We then plot the βk coefficients as well as the corresponding confidenceintervals to illustrate the trends in the dependent variable and the significanceof the changes in the trends. Figure 1 shows that there is a statisticallysignificant increase in the productivity measures for the establishments afterthe firm goes private, for all alternative measures. Further, consistent with theresults in Table 2, there appears to be a strong preexisting increasing trendin almost all of the measures, though the figures suggest a short-run slowingdown of productivity before going private and a short-run acceleration aftergoing private.
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2.2 The difference-in-differences methodologyThe before-after analysis simply summarizes the trends in the variables ofinterest in the plants belonging to the firms that went private. However, thesechanges could be driven by factors specific to the industry or age-relatedchanges. Changes may also be driven by factors related to the pre-event sizeof the establishment—for example, if going-private firms’ establishments wererelatively large, and if all large establishments experienced relatively differentpatterns of productivity change.
To rigorously address potential bias from these industry-, age-, and initialsize-related factors, we form a matched control group for each establishmentin the going-private sample. The importance of industry variables (suchas market structure, industry demand, and capacity utilization) for firmrestructuring outcomes has been demonstrated in the literature, particularlyby Kovenock and Phillips (1997) and Maksimovic and Phillips (2008). Theresults in these papers suggest that it is crucial to compare outcomes holdingindustry-level shocks constant, as we do using industry-age-size-year fixedeffects in the baseline analysis, and other industry-specific year effects in otheranalysis.
Specifically, for each establishment in the going-private sample, we selectup to eight matched control establishments in the following way to allow acomparison to an average matched control firm. Using the data for the closestprior to going-private year in which the establishment is observed in the ASM-CMF sample, we perform a two-way sort and classify all establishments intothree-digit industry-age quartile groups so that we have four groups for eachindustry. Then, we sort again by employment within each industry-age quartilegroup, and we select up to four non-going-private establishments just lower andup to four non-going-private establishments just greater in size than the going-private establishment. There are not always eight matched controls in caseswhere the going-private establishment was too close to the largest or smallestestablishment within the industry-age quartile. For a very small sample, lessthan 3% of establishments, control groups overlap. We dropped all such controlgroup establishments from our analysis, so that control groups are unique toeach going-private establishment.
This procedure generates nonoverlapping “cells,” with one going-privateestablishment and up to eight control establishments. We then estimate thefollowing regression specification:
where i refers to the plant, j refers to the cell that plant i belongs to, Djt refersto cell-year fixed effect, and the other variables are as defined in the before-after specification in Equation (1) above. Note that period dummy variables
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are defined only for the going-private sample—for instance, LR_PRE is adummy defined equal to one for going-private establishments in the three-year period from six to four years before going private (zero otherwise).Thus, the intercept term (β0) captures the overall mean value for the controlgroup of establishments. The inclusion of the cell-year dummies (Djt ) impliesthat the coefficients on the period dummy are estimates of the differencesbetween the going-private establishments and the control establishments, forthat period.
The differences between the period dummies yield difference-in-differencesestimates that control for cell-year, or equivalently, industry-age-size-yeareffects. As before, we are interested in the three estimates detailed inSection 2.1, but defined as a difference from the control group. This providesan estimate of the changes in differences between the going private and controlgroup in each defined period relative to the period just before the going-privateevent. If both the going-private establishments as well as their matched controlsexperienced similar changes in the dependent variable, then there would beno changes in the difference between the treated (i.e., going-private) andthe control group. The way that the control groups are constructed controlsfor any effects related to industry-wide, age-related, or initial size-relatedchanges.
We also test if the difference between the going-private and the control firmsis increasing or decreasing in the pre-going-private period. Though we matchon age and size characteristics just prior to the going-private event, differencesbetween the going-private and control groups could exhibit specific trends inthe prior period. One particular concern would be that, relative to this matchedcontrol group, the efficiency levels of the going-private establishments mayhave been on an upward trend; that is, the going-private establishments mayhave been selected based on prior trends. Then, any post-going-private changesmay simply be a reflection of these relative trends. Therefore, this test helpsto establish whether differential prior trends are a source for biasing estimateddifference-in-differences changes.
Columns 4 to 6 in both Panels A and B of Table 2 present the results ofthis analysis. For all inferences, we compute p-values based on standard errorsclustered by the industry-size-age cells. As in the first three columns of the twopanels, we regress labor productivity and two measures of TFP according to thevarious methods described in Section 1.2. We note four important results fromthis table’s panels. First, we find that there is no preexisting improving trendin any of the productivity variables prior to the going-private decision relativeto the control establishments included in the regression. This suggests that theearlier result of a trend in productivity for the private firms is also mimickedby the control group of establishments.
Second, the long-run and short-run pre- and post- labor and OLS TFP arehigher for going-private firms relative to the control group, as evidencedby the economically and statistically significant positive coefficients in the
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first four rows. However, the coefficients using the Blundell-Bond measureare not significant in Panel A and are small economically, but statisticallysignificant, in Panel B. Further, the productivity measures are significantlygreater in the pre-period for all three measures in both panels (except for theBlundell-Bond measure in Panel A). Broadly, these results indicate that going-private establishments are more productive relative to establishments of similarsize, age and industry before going private and remain more productive aftergoing private.
Third, there is no evidence of a short-run increase in productivity variablesfor the going private establishments when compared with the control group:SR_POST−SR_PRE is insignificant in all three specifications in both PanelsA and B. This indicates that while going-private firm establishments do have ashort-run increase in productivity after exiting the public markets (as reflectedin columns 1 to 3), so do the control group establishments that do not changetheir public-private status. Fourth, we do not find any evidence of a long-run productivity increase in going-private firm establishments, relative tothe control sample, since LR_POST−SR_PRE is insignificant in all threespecifications in both Panels A and B. Thus, we do not see any evidence thatpressures due to severe agency problems or shortsightedness for listed firmsare a drag on establishment-level productivity.
Again, as in the case of the before-after analysis, we also examine adifference-in-differences event study graph, by plotting coefficients on theindex dummies from a difference-in-differences version of Equation 2 inFigure 2. Figure 2 confirms the results in Table 2. First, the pre-going-private trend is flat, confirming that the pre-going-private productivity trendsare similar for going-private group and the control group. Second, thereis no statistically significant short-run or long-run improvement in relativeproductivity for the going-private establishments, compared to the pre-going-private productivity levels.
Going private may still have had positive productivity consequences if itwas the case that these firms were headed for a relative decline in productivity.In other words, could it be that going private enabled these establishmentsto match the performance of the control establishments, whereas withoutthat they would have performed relatively worse? We see no evidence forthis possibility in Figures 1 and 2. First, the going-private establishmentshave higher productivity before and after going private based on two ofthe productivity measures (labor and OLS TFP). Second, Figure 1 showsstrong improving trends in all productivity measures for the going-privateestablishments, so the prior absolute productivity trends do not foretell futuredistress. Third, none of the productivity measures in Figure 2 show anysignificant dip prior to going private; in fact, the trends are remarkably flatfor most of the measures from years –2 to 0. Thus, there is no evidence thatwithout going private, the going-private establishments would have suffereddeclines in productivity.
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2.3 Addressing endogeneity of the going-private decision andrelated selection-bias
One potential concern in our study is that the decision to go private is notrandom, and thus it is important that we control for the endogeneity of thegoing-private decision, as it may effect productivity changes. In particular, ifthe choice of firms to go private were based on some characteristics that predictfuture improvements in productivity, then the before-after results in Section 2.1are biased by this endogenous selection of going-private firms. The difference-in-differences approach in Section 2.2 controls for this issue, but only if thekey drivers of future productivity changes are related to one or a combinationof industry-, age-, or plant-size-related factors.
We therefore employ two alternative approaches to constructing the controlgroup. First, we utilize the results in Bharath and Dittmar (2010) to construct apropensity score matched control sample. By matching on the propensity score,we test whether the establishments that went private show an improvement overand above the improvement exhibited by firms that had a similar probability ofbeing selected into going-private treatment (Rosenbaum and Rubin 1985).
Bharath and Dittmar (2010) find that firms that ultimately go private are verydifferent and discernible in information and liquidity considerations, relativeto firms that remain public, at the time of the IPO. They estimate a logit modelusing explanatory variables only at the year following the IPO to predict ifa firm will ultimately go private. They find that firms that are more likely toultimately go private have less analyst coverage, less institutional holdings,more concentrated ownership, and more mutual fund ownership at the timeof the IPO compared to firms that remain public. They also find that firmsthat go private are more illiquid and have less share turnover, supportingthe importance of liquidity issues. Using a receiver operating characteristics(ROC) analysis, they show that the logit model has a 71% accuracy comparedto a benchmark of 50% accuracy with a random guess, reflecting substantialimprovement over a naive model. Bharath and Dittmar (2010) also show thatthe evolution of these firm characteristics affects the decision to go public.
Motivated by these results, we construct a sample of the firms with theclosest 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 theirestablishments as the matched sample controls. In calculating the propensityto go private, we use the firm-specific control variables at the time of the IPO,which is on average thirteen years prior to going private, as in Bharath andDittmar (2010). Specifically, we use log sales, analyst coverage, R&D, capitalexpenditures, a dividend dummy, turnover, market to book, free cash flow,leverage, cash, net fixed assets, and the number of past mergers to estimate theprobability a firm will go private, as in column 2 of Table 7 of Bharath andDittmar (2010). Since these control variables are not available for all firms inour sample, the number of establishments of firms that went private drops from28,518 in Table 2 to 22,488 in these estimations.
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One noteworthy attribute of this control group is that all of the control firmsare also listed firms, as the propensity model in Bharath and Dittmar (2010) usesstock market–related variables. This technique constrains the establishments inthe matched sample to have these data available as well. In Section 2.2, thecontrol group was establishments of all non-going-private firms, which includeboth listed and unlisted firms.
Similar to the approach in Section 2.2, we include industry-propensitycell-year fixed effects in each regression. Here a “cell” refers to the unique(i.e., nonoverlapping) group of establishments comprising one going-privateestablishment and the control group of matched establishments (by propensityscore and industry). Accordingly, as in Section 2.2, the estimated effects arethe mean of the relative difference between each going-private establishmentand its matched control group. For all inferences, we compute p-values basedon standard errors clustered by industry-propensity score cells in the table.
In addition to using the propensity match, we also employ a third matchingcriterion to address the potential bias from selection on past productivity.Kovenock and Phillips (1997) show that productivity is negatively relatedto restructuring decisions such as plant closures. In our context, if relativelymore productive firms went private, then there could be less scope forimprovements for these plants relative to matched plants that may have lowerinitial productivity. While the lack of differential prior trends in our baselinedifference-in-differences analysis assuages these concerns to some extent, onedirect approach to addressing this concern is to include ex ante productivity asone of the matching variables.8 Specifically, instead of establishment size usedin our baseline difference-in-differences approach, we use the Blundell-BondTFP measure and match each going-private plant to up to eight establishmentsclosest in this productivity measure, from within the same three-digit industryand age quartile.
The results from the difference-in-differences analysis using the twoalternative approaches are presented in Table 3, Panels A and B. Panel A ofTable 3 presents the results for the full sample, and Panel B presents theresults for the subset of firms that are not subsequently sold. The first threecolumns in both panels show that relative to firms with a similar propensity togo private, the sample firms exhibit no preexisting trend in productivity sinceSR_PRE − LR_PRE is insignificant in all three specifications in both Panels Aand B and the pre- and post-productivity of the going-private firm is no longersignificantly different from the control sample. More important, consistent withthe results in Table 2, the firms exhibit no significant improvement in eitherthe short-run or long-run period after going private; all the tests for changesrelative to SR_PRE are insignificant in both panels A and B). Thus, the null
8 We thank one of the referees for suggesting this method.
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Table 3, Panel AChanges in productivity measures around the “going-private” decision: DID specifications usingalternative propensity score and past TFP matched control groups
Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP
This table presents regression results for each of three productivity measures for the sample of establishmentsof firms that went private. In columns 1 to 3, for each establishment in the going-private sample, we constructthe closest propensity-to-go-private score-matched firm(s) but did not go private, as control firm(s). We use thefirm specific control variables at the time of the IPO to estimate the propensity to go private as in Bharath andDittmar (2010). Since these control variables are not available for all firms (whose establishments we considerin the regression), the number of establishments of firms that went private drops in these propensity-matchedestimations. In columns 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 ofManufactures (CMF) and the Annual Survey of Manufactures (ASM). Dummy variable LR_PRE equals 1 foryears –6 to –4 from the going-private date and 0 otherwise, SR_PRE equals 1 for years –3 to –1 from the going-private date and 0 otherwise, SR_POST equals 1 for years 0 to 3 from the going-private date and 0 otherwise,and LR_POST equals 1 for years 4 to 6 from the going-private date and 0 otherwise. p-values based on standarderrors clustered by industry-propensity cell year (columns 1–3) and industry-past TFP cell year (columns 4–6)are in parentheses.
improvement in productivity after going private is robust to using alternativecontrol groups to alleviate endogeneity concerns.
Columns 4 through 6 of both panels of Table 3 present the results fromdifference-in-differences estimation with the sample matched by past TFP. Herethere is evidence of a preexisting relative trend, with two of the three measures(labor and OLS TFP) showing an increase in productivity from the long-runperiod 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 beforegoing private, the going-private establishments’ productivity is on an upwardtrajectory. And, the pre- and post-productivity are significantly higher usinglabor and OLS TFP measures, since both pre- and post- LR and SR coefficientsare positive and significant in Panels A and B.
The coefficients on the Blundell-Bond measure are close to zeroeconomically or significant but negative. However, similar to the results usingthe other control groups, the going-private firms do not show significant relative
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Table 3, Panel BChanges in productivity measures around the “going-private” decision: DID specifications usingalternative propensity score and past TFP matched control groups, excluding sold plants
Labor OLS Blundell- Labor OLS Blundell-productivity TFP Bond TFP productivity TFP Bond TFP
The data used to construct this sample exclude all sold plants (i.e., those that underwent a change in ownershipafter the going-private date) in both the treatment and control samples. In columns 1 to 3, for each establishmentin the going-private sample, we construct the closest propensity-to-go-private score-matched firm(s) but did notgo private, as control firm(s). We use the firm specific control variables at the time of the IPO to estimate thepropensity to go private as in Bharath and Dittmar (2010). Since these control variables are not available forall firms (whose establishments we consider in the regression) in our sample, the number of establishments offirms that went private drops in these estimations. In columns 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 thesesamples are taken from the Census of Manufactures (CMF) and the Annual Survey of Manufactures (ASM).Dummy variable LR_PRE equals 1 for years –6 to –4 from the going-private date and 0 otherwise, SR_PREequals 1 for years –3 to –1 from the going-private date and 0 otherwise, SR_POST equals 1 for years 0 to 3from the going-private date and 0 otherwise, and LR_POST equals 1 for years 4 to 6 from the going-private dateand 0 otherwise. p-values based on standard errors clustered by industry-propensity cell year (columns 1–3) andindustry-past TFP cell year (columns 4–6) are in parentheses.
difference-in-differences improvement in productivity between the pre and postperiods. Specifically, in all specifications, there is no significant difference-in-differences increases in productivity, in both the short and long run. On theOLS TFP measure, establishments have a statistically significant decrease inproductivity after going private relative to the control group, but the economicmagnitude is small: –2.7% over the six year long-run period, translating to lessthan –0.5% per year. Overall, this evidence confirms that although firms thatgo private improve their productivity in absolute terms, this improvement isnot better than other similar firms.
3. Analysis of Other Outcomes
The previous results establish the fact that while labor and TFP productivityimproves for the going-private establishments, it does not improve differ-entially compared to matched peers. By implication, this analysis suggeststhat there is no evidence for either over- or underinvestment in listed firms,
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as suboptimal investments should result in a negative effect on operationalefficiency, and hence lead to improvements in productivity after going-private.
Nevertheless, it is interesting and informative to examine investment directly,and thus in this section we examine two related sets of outcomes. First, welook at capital and employment changes in establishments belonging to going-private firms. If empire building causes listed firms to invest excessively andexpand, we should expect to see less investment and corresponding employmentreductions at the establishment level after firms go private. Alternatively, iflisted firms sacrifice long-term growth by reducing investment and expansions,perhaps due to the pressure of a myopic market, we would expect the reverse.
Second, we examine a firm’s propensity to sell or close plants. Again, ifagency problems cause listed firms to expand or forgo selling and closingestablishments, we would expect to see a higher probability of selling off orshutting down plants after going private; and, if public markets lead firms tounderinvest, we would see the opposite.
3.1 Analysis of capital and employmentIn this section, we examine the capital and employment choices around thegoing-private decision. The regression specifications are identical to that inSection 2, except the independent variables are now log deflated capital and logemployment. Table 4 presents the regression results, and Figure 3 summarizesthe coefficients in the regression with the confidence intervals. We present fourspecifications: the before and after, the difference-in-differences matching aswe did in Table 2 (DID1), difference-in-differences matching as we did inTable 3 including by past TFP (DID2) and by the propensity to go private(DID3).
Examining the first four rows of Table 4, columns b to d, we find that going-private establishments invest more in capital and employment relative to firmsmatched by age, size, industry, and productivity, but this does not hold when wepropensity match (columns 1d and 2d). Recall that the propensity match usesstock market data and thus compares going-private establishments to matchedestablishments owned by listed firms. This result implies that the going-privatefirms invest more than comparable private but not public firms.
More important, examining rows 5 and 6, we find that while log deflatedcapital for the private establishments increases by 2.5% (specification 1a)in the short-run before-after comparison, it actually declines by 5% to 19%relative to all of the three control groups over this same period (specifications1b to 1d). Also, while in absolute terms capital increases by 6.4% in the longrun after going private, relative to the alternative control groups they showa large and significant decline of about 15% to 36%. In row 6, we find thatthere is a statistically significant upward trend in capital in the going-privateestablishments in absolute terms and relative to the control sample matchedby ex ante TFP, but find no a statistically significant prior trend relative to theother control groups.
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This table presents before-after and difference-in-differences regression results for capital and employment. InDID1, for each establishment in the going-private sample, we include up to eight establishments (based ondata availability) that are closest in size (employment) to the going-private establishment from within the samethree-digit SIC industry, and belonging to the same age quartile as controls. In DID2, for each establishment inthe going-private sample, we include up to eight establishments (based on data availability) that are closest inpast TFP to the going-private establishment from within the same three-digit SIC industry, and belonging to thesame age quartile as controls. In DID3, we include up to eight establishments within the same three-digit SICindustry closest in propensity to go private (but whose owner firms did not go private), as control establishments.The data used to construct this sample are taken from the Census of Manufactures (CMF) and the Annual Surveyof Manufactures (ASM). Dummy variable LR_PRE equals 1 for years –6 to –4 from the going-private dateand 0 otherwise, SR_PRE equals 1 for years –3 to –1 from the going-private date and 0 otherwise, SR_POSTequals 1 for years 0 to 3 from the going-private date and 0 otherwise, and LR_POST equals 1 for years 4 to 6from the going-private date and 0 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 employment shows a decline in absolute terms in both the short run(5.8%) and the long term (8.9%), shown in specification 2a. This seems in linewith a prior trend decline of 3.9%. This pattern of declines in employment isalso seen relative to two of the three control groups in specifications 1b and 1c.In specification 2d (propensity matched sample), while there is a decline, thechange is not significantly different from zero.
In summary, we find that, relative to control groups, establishments decreaseinvestment in capital and employment after going private.9 These resultssuggest that public markets lead firms to invest more than comparable privatefirms. Taken alone, the results for capital and employment suggest potential
9 In the context of the productivity results, one relevant question is why the relative downsizing on the input sidein capital and employment did not translate into productivity gains; in untabulated results we find that salesdeclined in line with the decreases in capital and employment, so that the input declines were not TFP enhancing.
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Capital and employment trends(5% confidence intervals)
Figure 3Before-after and difference-in-differences (DID) trends for capital and employment in 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 trends relativeto an industry-age-initial size matched control group. The confidence intervals are based on standard errorsclustered by plant in the before-after figures, and by control group cells in the DID figures.
overinvestment due to agency problems leading to empire building in the going-private firms when they were listed; however, this interpretation is not consistentwith difference-in-differences results showing that firms’ productivity doesnot change after going private, detailed in the previous section. If firms hadbeen overinvesting when they were public, we would have expected to see animprovement in productivity, but we find no evidence of such a change relativeto control groups.
3.2 Analysis of plant exit (shutdown and sales) decisionsIn this section, we examine if firms are more or less likely to shut down orsell establishments after going private. Because all of our analysis is basedon examining going-private establishments that were operational in the yearbefore going private, the analysis here will essentially examine the exit rate forgone-private establishments, relative to the industry-size-age control group. Inother words, we will be looking at differenced means, and we will not be doinga before-after or difference-in-differences analysis. In this analysis, we analyzeall exits as well as separately examine sales and closings of plants. Because theexit data derive from the LBD, it is not limited to manufacturing firms. Thus, inour initial analysis of exits, we include a much larger sample than that used inthe earlier tables. However, in our primary analysis of exit rates, we will control
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for productivity, which is available only for manufacturing establishments, thusagain limiting our sample to manufacturing firms.
3.2.1 Exit summary statistics. We begin with summary statistics of exit ratesby industry (analogous to Table 2 in Kovenock and Phillips 1997), variousindustry classifications (used in Maksimovic and Phillips 2008), time periods,and acquirer type.10 This analysis is similar to that presented in Maksimovic,Phillips, and Prabhala (2011), allowing us to compare our summary statisticsfor the going-private transactions to their much broader analysis of mergers andfull-firm acquisitions. First, presented in the Internet Appendix Table A.4, wepresent the percentage of establishments in the going-private sample that aresold or closed in the three to six years following the going-private transaction byindustry. As in Kovenock and Phillips (1997), the statistics show wide variancein exit rates as well as sale and closure rates across industries: within thesesixteen industries the exit rates range from 27% to 58% over the first three yearsand 48% to 76% over six years, with the paper industry having the highest exitrate and apparel and textiles having the lowest (in the first three years).
Table 5 illustrates how the exit rates vary over time and by type ofacquirer and industry classifications. In addition to this, we provide summarystatistics from Maksimovic, Phillips, and Prabhala (2011) labeled “MPP” forcomparison. The top section of Table 5 shows that the going-private firms exitplants at a higher rate than the control sample over the three and six year horizon.This pattern relative to controls is the same as in MPP, as they find greater exitand closure for target firm establishments as well. The exit rate in our sampleover the three-year horizon (36.5%) is lower than that in the much broaderMPP mergers and acquisitions (M&A) sample; interestingly, the closure rate ishigher 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 establishments exit strategy ismore reliant on closure than that of the typical M&A target establishments. Thepatterns are roughly consistent across acquirer types (top row) and industryclassifications (bottom row).
In the middle row, the split by the 1990s and 1980s shows greater exit(both by sale and closure) in the 1990s, which is consistent with the relativepatterns found by MPP. In the bottom row, we split the sample into fourindustry classifications. Not surprisingly, the largest amount of exits are foundin declining industries, both for the going-private sample and the controlfirms. These results, taken with those from MPP, illustrate that going-private
10 Specifically, as discussed in Maksimovic and Phillips (2008, 674): (i) In Consolidating industries, the changein long-run shipments is above the median industry change but the change in the number of firms is below themedian. (ii) In Technological Change industries, the change in long-run demand is below the median industrychange but the change in the number of firms is above the median. (iii) In Declining industries, the change inlong-run demand and the change in long-run number of firms are both below the median industry change. (iv) InGrowth industries, our excluded group, long-run industry shipments and the long run (which we define over theperiod 1981 to 2001) number of firms are increasing, and changes for each of these factors are above the medianindustry change.
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transactions, like mergers, are associated with firms selling off and closingestablishments at a higher rate.11 This evidence is consistent with firmsreversing the effects of empire building as described in Jensen (1986).
3.2.2 Exit hazard and propensity analysis. To examine how changes inestablishment characteristics and the private firm status affect the probabilityof establishment exits, we use a hazard model to investigate if and when a plantis closed down or sold. Specifically, we are interested in the length of time ittakes for a plant to be shut down or sold from the date of going private, andthe influence of different variables on that duration, controlling for the fact thatour comparison plants may also have exit decisions at some unobservable time.In the baseline case, we use the exponential proportional hazard model. Themodel 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 positivecoefficient on variable x in the hazard model implies that a higher x is linked tohigher hazard rate and thus a lower expected duration. The hazard ratio, whichis simply exp(β), tells us how much the hazard (i.e., instantaneous risk) of exitincreases for a unit change in the independent variable.12
In each estimation, the sample includes going-private establishments andmatched controls. As explained in Sections 2.2 and 2.3, each going-privateestablishment is matched to a unique set of control establishments; each ofthe matched controls are assigned the same “start/birth” year as the matchedgoing-private establishment. We use time-invariant characteristics (defined asat the year before going private) to explain duration, with the main variable ofinterest being the going-private dummy variable. We include establishment logemployment, age, two-digit industry, and year fixed effects as controls.13 Thatis, our goal is to understand whether the going-private establishments have ahigher hazard for exit relative to the control group, controlling for size, age,industry, and common year shocks.
Each plant we examine at the year before going private has two competingevent outcomes – sale or closure. We record only the earlier of the two outcomes;
11 One difference between our sample statistics and MPP is that the exit rates for our control group are much closerto 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, whileMPP look at all other firms within the industry. Exit patterns are likely to be more similar once we condition onage and size, as is well documented in the literature. And the hazard models in Table 6, Panel A, confirm, plantexit is strongly correlated with plant size and age.
12 In untabulated results, we checked and found our results to be very similar when using the Cox proportionalhazard model, where the baseline hazard h(t,0) is allowed to be arbitrary (instead of set to 1 in the exponentialmodel); the Cox partial likelihood estimator provides a way of estimating β without estimating h(t,0).
13 To clarify, for each establishment, there is only one observation, with the age and log employment defined as ofthe year before going private. For controls, the relevant year is the year before going private for the going-privatefirm they are matched to.
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Gone-private dummy 1.153 1.330 1.061 1.298 1.306hazard ratio
Control group Industry-size-age matched Industry-size-age matchedFixed effects Industry, Year Industry, Year Industry, Year Industry, Year Industry, YearNumbre of observations 203,311 183,329 203,311 203,541 203,311
Panel B: OLS with Three-year outcome dummy Six-year outcome dummycell-year fixed effects Exit Sold Closed Exit Sold Closed
This table presents exponential and competing risks model (Panel A) and linear propensity model (Panel B)results. In column 1 of Panel B, the three- (six-) year Exit dummy is a variable that equals 1 if the plant exited(was sold or closed) in the three (six) years after going-private and 0 otherwise. In column 2 of Panel B, thethree- (six-) year Sold dummy is a variable that equals 1 if the plant was sold in the three (six) years after thegoing-private date and 0 otherwise. In column 2 of Panel B, the three- (six-) year Closed dummy is a variable thatequals 1 if the plant was shut down in the three (six) years after the going-private date and 0 otherwise. (Thesevariables are undefined [missing] for a plant for time periods after it is shut down.) For both panels, for eachestablishment in the going-private sample, we include two establishments that are closest in size (employment)to the going-private establishment from within the same three-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 hasone observation for each of the gone-private and control establishments. Panels C and D, exclude industry-fixed effects in order to explore the role of industry-level variables. Capacity utilization measure is taken fromGorodnichenko and Shapiro (2011). Industry concentration is measured as in Kovenock and Phillips (1997) asthe market share of the top four plants in the industry. Change in output demand is defined as aggregate changein industry output. Industry classifications used in Panel D are based on definitions in Maksimovic and Phillips(2008). The data used to construct this sample are taken from the Longitudinal Business Database (LBD). PanelD includes variables from the Census of Manufacturers (CMF) and Annual Survey of Manufacturers (ASM).p-values based on standard errors clustering by control group cells are in parentheses.
thus, if a plant is sold, then it is no longer at risk of being closed and vice versa.In the baseline analysis of sale (closure) in columns 2 and 3 of Panel A ofTable 6, exit by closure (sale) is treated as censoring. As textbook discussionsof competing risks (e.g., Pintilie 2006) point out, treating the competing event ascensoring can be misleading, and ideally we need a framework that adequatelycounts subjects that experience the competing event as not having any chance ofexperiencing the alternative event. We use the competing risk model proposedby Fine and Gray (1999), which allows for assessing the effect of covariateson the subhazard for each of the individual events.14
14 This is implemented using the Stata command stccreg. We thank the editor for suggesting this approach.
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Fixed effects Year Year Year Year Year YearNumber of observations 9,380 9,380 9,380 8,363 8,363 8,363
The results of these analyses are presented in Table 6, Panel A. The first threecolumns present the 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 a statistically higher hazard rate of both closing and sellinga plant. Specifically, these establishments have a 28% (6%) higher hazard rateof 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 to30% significantly higher hazard rate of closing or being sold; the competingrisk analysis suggests that the lower hazard for closure is downward biased bytreating sales events as equivalent to censoring.
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The main strength of the hazard rate model is that it explicitly accounts forthe sizeable right censoring that occurs in analysis such as ours (i.e., a sizeablenumber of establishments survive and are neither sold nor closed until thelast year of the dataset). However, we recognize that it is not computationallyfeasible to use cell-year fixed effects in a hazard model, similar to how wedid in the previous section. Thus, in order to check robustness to using higherdimensional fixed effects, we repeat this analysis using a linear propensitymodel in Panel B.15 To do this, we first define two exit variables: three- (six-)year exit dummy is a variable that equals one if the plant exited through a sale orclosure 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 tomatched control establishments in the same cell. We find that establishments ofthe going-private firms have a 3.1% (5.1%) higher propensity than the industry-age-size matched control group to shut down a plant within the next three (six)years, immediately after going private. As in the hazard analysis, the samepatterns hold for both sale and closure: sale (closure) rate is 1.5% (1.5%) higherover the three-year window, and 4.3% (0.8%) higher over the six-year window,with all estimates significant at the 1% level.
The analysis in Panels A and B control for fixed and time-varyingindustry effects, which subsumes the effect of specific industry characteristics.To understand the effect of specific industry characteristics and relate toprevious literature that examined closures and sales, we repeat the propensityanalysis including only year fixed effects and covariates for ex ante (yearprior to going private) productivity, employment, number of plants, industrycapital utilization, industry concentration, change in industry aggregate outputdemand, and industry classifications. The capacity utilization measure is takenfrom Gorodnichenko and Shapiro (2011), who use the U.S. Bureau of Census’ssurvey of capacity utilization to construct industry-specific capacity utilizationmeasures. Industry concentration is measured as in Kovenock and Phillips(1997) (KP1997), 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 Maksimovicand Phillips (2008) (MP2008). KP1997 and MP2008 find these factors to beimportant determinants of plant exits.
These results are presented in Panels C and D of Table 6. Because we controlfor variables available only for manufacturing firms, the results in Panels Cand D also provide evidence of the robustness of the exit analysis for thissubsample. We find that in each specification and across both the three- and
15 This approach has the drawback that it does not address the right censoring issue; however, in robustness checks,dropping plants that survive to the end of the data period yielded very similar results to those reported in Panel B,which provides reassurances that the qualitative conclusions are not biased by the right-censoring of the data.
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six-year horizons, going-private establishments have a significantly higher rateof being sold or closed. Specifically, going-private establishments have a 4.7%to 7.1% higher probability of being sold and a 3.9% to 5.3% higher probabilityof being closed than the matched sample. Consistent with the findings in Table 3of KP1997, plant closure (column 3 in Panels C and D) is generally negativelycorrelated with plant productivity, size of firm (measured as firm employment),and change in output demand. Interestingly, and consistent again with KP1997,the number of plants owned by the firm is positively correlated with closure;thus, conditional on firm employment, a plant belonging to a firm with moreplants is more likely to be closed.
As in KP1997, we find negative effects of capacity utilization and laggedindustry concentration, but these are weaker and not statistically significant.The pattern is similar for sales (column 2) with respect to firm employment andnumber of plants owned by the firm, but labor productivity is not stronglycorrelated with sale propensity (so more or less productive plants are notsystematically targeted for sale). And, change in industry demand and laggedindustry concentration are weakly positively correlated with sales over both thethree- and six-year horizons. In Panel D, over a six-year horizon, we find thestrongest closure rates for declining industries, which is consistent with whatcould 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 shutdowns after going private. Again,this evidence is consistent with reversing the effects of empire building in listedfirms but does not support a myopia-related hypothesis that the elimination ofthe stock market’s short-term focus makes it more likely for firms to nurtureplants after going private.
3.2.3 Selection of plants for closure. In this subsection, we examine ifthe going-private firms differentially target the poorly performing plants (ina labor productivity and TFP sense) for closure. There are two motivationsfor doing this analysis. First, this analysis could help us understand themotivations for going-private transactions, given that our earlier results showno gains in productivity at the establishment level. If firms differentially targetlower-productivity establishments for closure, this could help them to retainhigher-productivity plants in their portfolio.16 Second, this evidence providesinsight into the effect of public markets. If listed firms partake in empirebuilding, we may expect that many unproductive plants were not closed whenthe firm was public; thus, we predict a more negative effect of productivity onthe decision to close or sell a plant after the firm goes private. Alternatively, if
16 Also, while we do not have the data to verify this, firms may be selling the plants or assets of closed plants abovethe pricing implicit in the going-private transaction to gain value. Consistent with this, in untabulated results, wefind some evidence that going-private firms targeted plants in higher income areas for closure (presumably landand building in these areas would be the most valuable).
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the stock markets are myopic, we may expect to see market pressures leadingto stronger targeting of worse-performing plants by public firms (which mayneed to be nurtured to achieve greater productivity levels).
To test for differential targeting, we add the productivity measures, as well asan interaction term between the going-private dummy and productivity, to thehazard and OLS model specification in Table 6 (Panels A and B). Results arepresented in Table 7. The control group is composed of establishments matchedon industry, age and initial size (as explained in Section 2.2). The hazard modelin Panel A of Table 7 includes controls for employment and age (not reportedfor brevity), and industry and year fixed effects, while the OLS model in PanelB includes much more detailed industry-size-age cell-year fixed effects.
Across all specifications, the coefficient estimates on the interaction term arealways negative when we look at the summary exit variable (column 1) andsales decisions (column 2). We find the same for closure with one exception,OLS FE TFP in Panel A. The effects are statistically significant in about halfof the cases, with magnitudes on the interaction term for exit in Panel B forlabor productivity (Blundell-Bond TFP) suggesting that plants with a 10%lower than mean productivity would have a 0.4% (1%) higher propensity to beexited (closed or sold). The magnitudes suggest a stronger targeting for salesin Panel A, as magnitudes for cross-terms for sales are higher, but this is trueonly for the Blundell-Bond TFP measure in Panel B.
Interestingly, across all the measures, the average effect of productivity forsales (column 2) is positive and significant in Panel A and close to zero onPanel B. This indicates that low-productivity establishments are not targetedfor sale on average, but the negative interaction term shows that going-privatefirms more aggressively target lower-productivity plants for sale, consistentwith 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 presented in Table 6.This suggests that productivity is indeed informative and guides the plantshutdown decisions of all firms. Second, and more important for our study,the coefficient on the interaction term of the productivity variables with thegoing-private dummy is generally negative, though not statistically significantin most cases. This suggests weak evidence that going-private firms also moreaggressively target less productive establishments for closure, again consistentwith attempting to improve productivity of their remaining portfolio and reversethe effect of empire building and possibly contradicting the role of myopia.
3.3 Results by acquirer typeThere are multiple ways a firm can go private, and our sample includestransactions that are driven by private equity firms, management, and privateoperating firms. In the main analysis, we treat these deals uniformly.
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However, it is possible that the productivity dynamics, as well as capital/investment and shutdown choices, differ by the parties involved in thetransaction. To study this, we classify the sample firms that went private intothree categories: a buyout by a private operating firm, a buyout by a privateequity firm, a buyout by the management. We source the classifications forthese deals using news reports from Factiva. Specifically, we read news articlesregarding each deal and classify each into categories based on parties involvedin the transactions. When it is unclear, we classify the deals as unclassified. Thecategory types are nonexclusive, so some deals may are classified under morethan one type.
This analysis helps evaluate two alternative explanations for the resultsdocumented thus far. One potential explanation is that going-privatetransactions are highly leveraged and this leads to short term pressures evenafter going private, which in turn prevents the firm from adopting long-termstrategies to improve productivity and pressures the firm to downsize andclose/sell plants. Because high leverage is more likely to occur in private equityand management buyouts, the operating firm takeover subsample will allow usto examine results less likely to be confounded by effects of high leverage.17
Second, independent of the extent of leverage, investors driving going-privatetransactions may have short horizons. Again, this is more likely to hold forprivate equity transactions than for operating firm buyouts.
Table 1, Panel B, shows that there are 819 establishments associated with amanagement buyout, 1,207 with an operating firm and 958 with a private equityacquirer, similar to the relative breakdown in Bharath and Dittmar (2010). Thus,45% of our sample consists of operating firm acquisitions, which are less likelyto be affected by both leverage effects and private equity myopia effects.
In Table 8, we present comparisons of analysis of productivity that are similarto 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 onindustry, age, and initial size (as explained in Section 2.2). The same analysisusing establishments within three-digit industry propensity-to-go-private scorematched control group (described in Section 2.3) yield qualitatively similarresults but are omitted here for brevity; they are available on request fromthe authors.
Table 2 shows that on average going-private establishments did not improvebased on any measure of productivity relative to a sample matched by industry,size, age, and year. In general, Table 8 confirms this finding across each
17 To confirm the assumption that operating firm acquisitions do not have significant increases in leverage, wemanually collected leverage data for a random subsample of thirty such events for the year 1999 from the CapitalIQ database. For comparison, Guo, Hotchkiss, and Song (2011) have twenty-one buyout firms in 1999 withpost-buyout data in their sample, which they used to conclude that leverage increases dramatically after buyoutevents. We then examined average long-term debt to equity for the period before and after the acquisitions. Wefound that mean (median) leverage ratios for this subsample actually drops by –5% (–1.25%). This confirms thatoperating firm acquisitions were not accompanied by large increases in debt, so effects found for this sample ofoperating firm acquisitions are unlikely to be influenced by effects of excess leverage.
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acquirer type. Specifically, no acquirer type shows any significant differencein labor productivity or TFP using the OLS methods. There is weak evidencethat unclassified and management-led buyouts had a decrease in the short-run period after going private. But, this result is inconsistent across differentproductivity measures. The only positive and significant change in productivityare for those taken private by an operating firm using the Blundell-Bond TFPmeasure and only in the longer run. Again, the result is not consistent acrossTFP measures.18 Overall, the evidence in the subgroup analysis is consistentwith the full sample for each of the subgroups. Most important, there is noevidence that operating firm takeovers, which are least susceptible to pressuresfrom high debt or impatient investors, show any improvements in productivity.
Table 4 showed evidence of a decrease in both labor and capital investmentfor the full sample. In Table 9, we find similar evidence of a decrease in capitalinvestment across all acquirer types. Thus, the decrease in investment is notdriven by one subgroup, and again operating firm takeovers show significantcapital decline as well. The decrease in employment is consistent in sign butnot significant for operating firms, while management and private equity dealsshow significant declines in employment in the long run. Interestingly, there areno significant declines in employment in the short run for private equity firms.
In Table 10, we repeat the analysis in Table 6 (Panels A and B). The resultsshow that management acquirers are in fact less likely to exit, which is drivenby their much lower propensity to close plants, although they do have a higher-than-control-group propensity to sell plants. Operating and private equityacquirers show a greater propensity to both sell and close plants comparedto their control groups. These results are consistent across the hazard analysisin Panel A and the linear probability analysis in Panel B.
Finally, we replicate the OLS analysis in Panel B of Table 7, examiningwhether particular types of acquirers were more aggressive in targeting lower-performing plants for exit. We present these results in the Internet Appendix,Table A.6. In untabulated analysis, we repeat this using the hazard model ofPanel A, Table 7, and get similar results.
We find that the operating acquirers more aggressively targeted low-labor-productivity plants for both sales and closures; the results for sales aresignificant with the other two TFP measures as well, but the closure results aresmall and insignificant with Blundell-Bond TFP. The interaction term is alsosystematically negative, though not always statistically significant for privateequity acquirers, suggesting weak evidence that these acquirers were also moreaggressive in exiting low-productivity plants. Interestingly, the estimates aresmaller and insignificant for management acquirers; this group does not seemto be more aggressive in selling or closing low-productivity plants. The resultssuggest that both operating and private equity acquirers may be partly motivated
18 Further, in the Internet Appendix, Table A.5, we find that this positive result is not robust to matching on ex anteTFP.
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by potential gains (perhaps from high selling prices) through more aggressiveexit of poorly performing plants, while management does not appear to use thisstrategy.
The analysis on exits in Table 9 and in the Internet Appendix, Table A.6,provide some evidence that private equity acquirers are aggressive aboutexiting; however, the magnitudes of effects are similarly large for operatingacquirers as well, so the incentive from having a short investment horizon isnot the sole explanation for more aggressive exit found in the full sample.
4. Other Robustness Checks and Discussion of Results
In this section, we discuss a number of robustness checks and other tests thathelp to validate and explain the conclusions from the baseline analyses. Resultsare available on request from the authors, unless otherwise stated that they areincluded in the Internet Appendix.
(i) Examining outcomes at the acquirer firm: The above analyses documentthat there was no change in productivity, relative declines in employment andcapital, and an increased probability of sales or shutdown at the going-privateestablishments relative to control groups. However, we have not examined theoutcomes at the acquiring entity. To investigate this, we use firm ownershipidentification data in the Census LBD dataset to identify the acquirer firm andits establishments at the time of the going-private event.19
We investigate three sets of outcomes. First, it could be that the acquirerfirm expands operations in its establishments, offsetting the declines in inputsdocumented in the going-private establishments. We analyze changes in capital,employment, and sales at the acquirer firm establishments following the sameapproach as used for analyzing the going-private establishments, using a controlgroup 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 atacquirer firm plants, we found no difference-in-differences increases in capital,employment, or sales at the acquirer firm plants, both overall as well as in asample restricted to be in the industry of the target going-private firm. In fact,in most cases, we found significant difference-in-differences declines in inputs.
Second, it could be the case that the acquiring entity opens newestablishments in the same industry as the plants shutdown at the target going-private firm. We analyzed the propensity to open a new establishment andfound that this propensity declines significantly in absolute terms at the acquirerentities, and shows no differential change relative to a control group.
19 Firm 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 establishmentwe track the firm identifier and identify the first time it changes after the going-private event. We then define asthe acquirer firm the modal new firm identifier across all the acquired plants within a going-private firm.
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Third, we checked whether TFP went up at acquirer establishments (perhapsfrom a transfer of clients or markets that the acquirer’s plants were ableto serve without increasing inputs proportionately). We found no significantdifference-in-differences changes in any of the three baseline productivitymeasures at the acquirer firms’ establishments, in either the short term or thelong term. Thus, we conclude that there is limited evidence for changes atthe 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: Asa further check to rule out noise in productivity measures as an explanationfor the baseline results, we examine two operating profit measures: (i) grossoperating 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 operatingprofits to sales. We find results (presented in the Internet Appendix, Table A.7)that are very similar to those in the baseline productivity analysis; neitherprofitability measure shows significant improvement after establishments goprivate, relative to the two control groups. We also examine the ratio of differentcost components (materials, energy, blue-collar wage bill, and white-collarwage bill) to sales, and find no significant difference-in-differences changes inany of these components.
(iii) Splitting the sample over time: It is possible that the responsiveness ofmanagers to the stock market’s short-term focus may have been exacerbatedby the increasing use of stock options in executive compensation. Similarly,if leverage changes were driving any of our results, the use of leverage wasmuch higher in private equity deals in the 1980s than in 1990 and beyond. Totest whether the productivity and investment responses to going private havechanged over time, we repeat the analysis in Tables 2, 3, 4, and 5 separatelyfor the going-private transactions that occurred before and after 1992. Inuntabulated results, we find no notable differences between these samples; thequalitative conclusions of the analysis are the same for these samples separatelyas it was for the overall sample.
(iv) Relation to findings in Bharath and Dittmar (2010): Bharath and Dittmarexamine why firms go private, tracking firms from their IPO to the time they goprivate. They employ both a hazard model using the full panel of data as well asa logit model using explanatory variables only at the year following the IPO topredict if and when a firm will ultimately go private. As mentioned earlier, theyfind that despite the fact that, on average, the private sample firms remain inthe public market for more than thirteen years, firms that ultimately go privateare very different and discernible in information and liquidity considerations,relative to firms that remain public, throughout their public life and even at thetime of the IPO.
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Bharath and Dittmar also find that the changes in the characteristics over timeare important. Bharath and Dittmar show this by implementing a hazard modelutilizing a control sample that is matched by firm characteristics at the timeof the IPO. They find that firms that are more likely to ultimately go privatehave less analyst coverage, lower institutional holdings, more concentratedownership, and more mutual fund ownership at the time of the IPO and thatthese characteristics change over time to increase the hazard rate of goingprivate. Further, in the hazard model compared to the matched sample, firmshave 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 myopiain the decision to go private, their results that the decline in market to book,turnover, and other measures leave open the possibility that capital marketmyopia or agency conflicts could affect the decision to go private. In this paper,we extend their findings to directly test for the effect of capital market myopiaand agency problems on firms when they are private relative to when theyare public.
(v) Pre-delisting (SR_PRE) differences between going-private firms andpeers across alternative measures: In columns 4 and 5 of Table 2, Panel Aand Panel B (analysis using industry-size-age matched controls), as well incolumns 4 and 5 of Table 3, Panel A and Panel B (analysis using industry-past Blundell-Bond TFP matched controls), the going-private plants havehigher labor productivity and OLS TFP measures in the short run pre-delistingperiod (SR_PRE). However, the pre-period productivity is very small usingthe Blundell-Bond TFP measure and insignificant in Panel A of Table 2 andboth panels of Table 3. Further, all three measures of productivity are smalland insignificant in columns 1 to 3 in both panels of Table 3, where the controlgroup is matched on industry-propensity score.
The reason for discrepancies in economic magnitude and significance ofpre-delisting productivity across different measures is that going-private firmshave higher levels of capital in the short-run pre-delisting period relative tocontrol groups for both the industry-age-size and industry-past TFP matchedanalysis; this is evidenced by the large, positive, and significant coefficient forcapital on SR_PRE in columns 1b and 1c of Table 4. And, because both thelabor productivity and OLS TFP measures place lower emphasis on capital(as discussed in Section 1.2), this will cause differential results between thethree measures. In column 1d of Table 4, for the industry-propensity matchedapproach, there is no significant difference in capital level between going-private establishments and controls on SR_PRE; thus, all three measures yieldsimilar results in columns 1 to 3 of Table 3, Panels A and B.
(vi) Other checks: We perform a number of other checks of the baselineresults. These include (a) checking our results to using plant fixed effects
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and industry-year effects (instead of cell-year) effects in our difference-in-differences specifications; (b) checking the split-by-acquirer-type results inTable 8 (Panels B and C) using the event time figures; (c) redoing the baselineresults adjusting for sampling weights (the baseline analysis treats the ASM-CMF sample as an unbalanced panel) to check if sampling systematically affectsthe going-private sample differentially relative to the control groups. We findour results robust to these checks.
5. Conclusion
In this paper, we examine the effect of going private on a firm’s efficiency andinvestment. In doing so, we shed light on the effects of market constraints, suchas the separation of ownership and control and other public market pressureson firms. Using detailed plant-level data on firms that go private and a matchedsample, we present three key findings. First, going-private establishments aremore productive before and after going private relative to age, size, industry,and productivity matched control groups.
Second, there is no increase or decrease in the productivity of going-privateestablishments relative to any matched sample, although raw productivitydoes increase. Specifically, putting these results together, our key finding isthat while there is evidence for substantial within-establishment increasesin productivity after going private, there is little evidence of difference-in-differences efficiency gains relative to a peer group of establishmentsconstructed to control for industry, age, initial size (at the time of going private)and the endogeneity of the going-private decision effects. And, third, there issignificant restructuring activity of the firm’s portfolio of assets, as going-private firms sell and close significantly more plants than other firms anddecrease employment and capital investments.
These results clearly show that while going-private firms may be chosen froma distribution that differs from the population of establishments and partakein substantial restructuring, these firms do not implement changes that haveany effect on productivity relative to comparable firms. The differences in therestructuring activities provide evidence consistent with the reversal of empirebuilding that occurred while the firms were public and thus potentially illustratesthe effect of agency problems on listed firms. However, these reversals donot lead to improvements in productivity within retained establishments. Theresults also suggest that other constraints on public firms, such as marketmyopia, do not affect the productivity or lead to underinvestment by listedfirms.
Our results both complement and contrast Kaplan (1989a) and Long andRavenscraft (1993), who find improvements in investment and accountingprofits after an LBO. Similar to their studies, we find that there is an increasein productivity in our before and after analysis and a decrease in capitaland employment after going private relative to comparable firms, similar to
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Kaplan (1989a). The contribution of our study is that by employing Censusdata and a careful matching procedure, we are able to show that going privatedid not likely cause this increase in productivity, as the change is not significantonce compared to an ex ante similar sample. However, the data, sample, andcomparison firms in both of these papers differ from those employed here, socomparisons between our results and theirs must be done with caution.
With this caveat in mind, our findings suggest that using matching techniquesto control for the industry, size, age, and productivity, as well as the propensityto go private, the improved productivity found in these earlier papers and shownin our before-after analysis is not due to the change in the agency problems orpossibly capital market myopia of no longer being a public firm. We thereforeconclude that agency problems and other constraints in public markets lead tono greater effect on productivity than that experienced by similar private firms,though agency problems do lead public firms to overinvest, which is reversedafter going private.
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 our analysis,we use data from the ASM that surveys: (i) All establishments with greater than (or equal to) 250employees; (ii) All establishments of multi-unit firms; and (iii) a stratified randomized sample ofestablishments with less than 250 employees. For certain small establishments in the CMF, theemployment data is imputed based on reported payroll from administrative records data. Followingthe practice in the literature, such establishments (which are flagged by an “Administrative Records”dummy variable) are excluded from our analysis. As very few (less than 1%) of the going-privateestablishments belong to this category, this exclusion has very little effect on our sample size.
Key variables used in the analysis are as defined below. Deflators used for obtaining real valuesare 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 four-digit SICindustry-specific output deflators.
(b) Log real value added is defined as log of (real sales – real materials – real energycosts).
(ii) Input measures
(a) Log employment is the log of the total number of employees reported in theASM-CMF database.
(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 otherenergy sources used.
(d) Log real capital is defined as the log the real depreciated capital stock. The realdepreciated capital stock is constructed using the perpetual inventory method.The depreciation rates (and deflators) used to construct the plant specific realdepreciated structures and equipment stocks were taken from Becker and Gray(2009).
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(iii) Blundell-Bond system-GMM TFP measure Basic definitions of the productivitymeasures are provided in the text. Here we elaborate on the specific methodology usedto define the Blundell-Bond total factor productivity measure (more detail is available inBlundell and Bond (2000), and Bond and Söderbom 2005).
As in Blundell and Bond (2000), we assume a gross output production function with anAR1 component in the productivity term:
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 t
but do not depend on the fixed effect fi , one can obtain additional moment conditions thatuse lagged first differences as valid instruments for the equation in levels which greatlyimprove upon the properties of the estimator. These conditions are:
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 Söderbom (2005) show thatthe Blundell and Bond (2000) estimator addresses a critique of the OP and LP approachesput forth by Ackerberg, Caves, and Frazer (2006).
References
Ackerberg, D., K. Caves, and G. Frazer. 2006. Identification of production functions. Working Paper, Universityof California, Los Angeles.
Asker, J., J. Farre-Mensa, and A. P. Ljungqvist. 2012. Comparing the investment behavior of public and privatefirms. ECGI Working Paper Series in Finance, no. 282/2010.
1974
at University of W
indsor on July 14, 2014http://rfs.oxfordjournals.org/
Do Going-Private Transactions Affect Plant Efficiency and Investment?
Bebchuk, L. A., and L. A. Stole. 1993. Do short-term objectives lead to under- or over-investment in long-termprojects? Journal of Finance 48:719–29.
Becker, G., and W. Gray. 2009. NBER-CES Manufacturing Industry Database.
Bharath, S. T., A. Dittmar. 2010. Why do firms use private equity to opt out of public markets? Review of Finan-cial Studies 23:1771–1818.
Bhojraj, S., P. Hribar, M. Picconi, and J. McInnis. 2009. Making sense of cents: An examination of firms thatmarginally miss or beat analyst forecasts. Journal of Finance 64:2361–88.
Blundell, R., and S. Bond. 2000. GMM Estimation with Persistent Panel Data: An Application to ProductionFunctions. Econometric Reviews 19:321–40.
Bond, S., and M. Soderbom. 2005.Adjustment costs and the identification of Cobb Douglas production functions.IFS Working Papers W05/04, Institute for Fiscal Studies.
Davis, S., J. Haltiwanger, R. Jarmin, J. Lerner, and J. Miranda. 2008a. Private equity and employment. InA. Gurung and J. Lerner (Eds.), Globalization of alternative investments. Working Papers, Vol. 1: GlobalEconomic Impact of Private Equity, World Economic Forum.
———. 2008b. Private equity, jobs and productivity. Working Paper, University of Chicago.
Fine, J. P., and R. J. Gray. 1999. A proportional hazards model for the subdistribution of a competing risk. Journalof the American Statistical Association 94:496–509.
Gorodnichenko, Y., and M. Shapiro. 2011. Using the survey of plant capacity to measure capital utilization.Discussion Paper 11-18, U.S. Census Bureau, Center for Economic Studies.
Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate financial reporting.Journal of Accounting and Economics 40:3–73.
Greenstone, M., R. Hornbeck, and E. Moretti. 2010. Identifying agglomeration apillovers: Evidence from winnersand losers of large plant openings. Journal of Political Economy 118:536–98.
Griliches, Z., and J. Mairesse. 1995. Production functions: The search for identification. NBER Working Paperno. 5067.
Guo, S., E. Hotchkiss, and W. Song. 2011. Do buyouts (still) create value? Journal of Finance 66:479–517.
Jarmin, R. S., and J. Miranda. 2002. The longitudinal business database. Technical Report, U.S. CensusBureau.
Jensen, M. C. 1986.Agency costs of free-cash-flow, corporate finance, and takeovers. American Economic Review76:323–29.
Jensen, M. C., and W. H. Meckling. 1976. Theory of the firm: Managerial behavior, agency costs and ownershipstructure. Journal of Financial Economics 3:305–60.
Kaplan, S. N. 1989a. The effects of management buyouts on operating performance and value. Journal ofFinancial Economics 24:217–54.
———. 1989b. Management buyouts: Evidence on taxes as a source of value. Journal of Finance 44:611–32.
———. 1991. The staying power of leveraged buyouts. Journal of Financial Economics 29:287–314.
Kovenock, D., and G. M. Phillips. 1997. Capital structure and product market behavior: An examination of plantexit and investment decisions. Review of Financial Studies 10:767–803.
Lehn, K., and A. Poulsen. 1989. Free cash flow and stockholder gains in going-private transactions. Journal ofFinance 44:771–87.
Levinsohn, J., and A. Petrin. 2003. Estimating production functions using inputs to control for unobservables.Review of Economic Studies 70:317–42.
Lichtenberg, F. R. 1992. Corporate takeovers and productivity. Cambridge, MA: MIT Press.
1975
at University of W
indsor on July 14, 2014http://rfs.oxfordjournals.org/
Lichtenberg, F. R., and D. Siegel. 1991. The effects of leveraged buyouts on productivity and related aspectsof firm behavior. Journal of Financial Economics 27:165–94.
Long, W., and D. Ravenscraft. 1993. LBOs, debt, and R&D intensity. Strategic Management Journal, 14:119–35.
Maksimovic, V., and G. Phillips. 2001. The market for corporate assets: Who engages in mergers and asset salesand are there gains? Journal of Finance 56:2019–65.
———. 2002. Do conglomerate firms allocate resources inefficiently across industries? Journal of Finance57:721–67.
———. 2008. The industry life cycle, acquisitions and investment: Does firm organization matter? Journal ofFinance 63:673–708.
Maksimovic, V., G. Phillips, and N. R. Prabhala. 2011. Post-merger restructuring and the boundaries of the firm.Journal of Financial Economics 102:317–43.
Marschak, J., and W. Andrews. 1944. Random simultaneous equations and the theory of production.Econometrica 12:143–205.
McGuckin, R. H., and S. V. Nguyen. 1995. On productivity and plant ownership change – New evidence fromthe Longitudinal Research Database. Rand Journal of Economics 26:257–76.
Olley, S., and A. Pakes. 1996. The dynamics of productivity in the telecommunications equipment industry.Econometrica 64:1263–98.
Pintilie, M., 2006. Competing risks: A practical perspective. Chichester, UK: Wiley.
Porter, M. E., 1992. Capital disadvantage:America’s failing capital investment system. Harvard Business Review,September–October.
Rosenbaum, P., and D. Rubin. 1985. Reducing bias in observational studies using subclassification on thepropensity score. Journal of the American Statistical Association 79:516–24.
Stein, J. C. 1989. Efficient capital markets, inefficient firms: A model of myopic corporate behavior. QuarterlyJournal of Economics 104:655–69.
1976
at University of W
indsor on July 14, 2014http://rfs.oxfordjournals.org/
