Managerial Efficiency: A Study ofManagement Buyouts*

KIRAN VERM A University of Massachusetts-Boston

Abstract. Aggregate measures of firm performance such as market value and netincome are less informative about the operating perfonnance of a firm because theyincorporate many other factors in addition to the direct outcomes of operating deci-sions. This study develops and uses a more direct measure of operating performancethat is based on the total factor productivity of the firm. This measure is used to test forthe presence of real gains in efficiency for management buyouts. Results for a sampleof MBO firms in the manufacturing sector do not support the hypotheses of enhancedmanagerial efficiency. Relative productivity one and two years after the MBO is belowthe level one year before the MBO. Furthermore, relative changes in productivity forthe MBO sample of firms are less than the average for the industry. These results arecontrary to earlier results, which were based on profit margins alone.

Resume. Les mesures combin^es de la performance des entreprises telles que la valeurmarchande et le b6n6Tice net livrent moins d'information au sujet du rendement deI'exploitation de l'entreprise parce qu'elles incorporent de nombreux autres facteuis,outre les resultats directs des decisions d'exploitation. L'auteur a £labor£ une mesureplus directe du rendement de Texploitation reposant sur la productivity totale des fac-teurs de l'entreprise, dont il se sert pour verifier la presence de gains r^els d'effidence ila suite de radiats d'actions par les cadres. Les rdsultats obtenus pour un Echantillond'entreprises du secteur manufacturier dont les actions ont 6t6 rachetdes par les cadresne confinnent pas I'hypothfese d'une effidence accrue de la gestion. La productivity re-lative, un an et deux ans aprds le rachat, est inf^rieure h la productivity observ^e un anavant le rachat. Plus encore, les variations relatives de la productivity des entreprises,constituant l'^chantillon, dont les actions ont 6ti rachet^es par les cadres sontinfdrieures h la moyenne du secteur. Ces resultats sont en contradiction avec ceux detravaux ant^rieurs fond^s strictement sur les marges b^n^fidaires.

Evaluating managerial performance is one of the main objectives of users ofaccounting information. Traditionally, managerial performance has been mea-sured by observing changes in market value, earnings per share, return oninvestment (ROI), retum on assets (ROA), and gross margin. A measure ofmanagerial performance is informative if it is based on factors that reflect out-

* The author is grateful for the helpful comments of R. Kaplan, K. Palepu, P. Healy, K.Merchant, P. Asquith, and H. Falk. Any errors are entirely her own.

Contemporary Accounting Research Vol. 10 No. 1 (Fall 1993) pp 179-204 *CAAA

180 K. Verma

comes of managerial decisions. From this perspective, aggregate measuressuch as the market value or earnings per share are less informative aboutoperating performance than operating income because they refiect many fac-tors besides the direct outcomes of operating decisions. But operating incomecan still be considered a "noisy" measure because it incorporates both thecontrollable and the uncontrollable portions of operating performance.^Therefore, focusing on operating profit alone may not give enough insightinto the quality of managerial decisions about operations.

This study examines the issue of managerial performance by developing ameasure refiecting the operating efficiency of a firm.^ The measure developedin this study is based on the total factor productivity (TFT) of a firm, which ismeasured by adjusting firm profits for average changes in specific prices forthe outputs and inputs of the firm measured at the industry level. As discussedlater, this operationalization of TFP is designed to measure only the control-lable portion of operating performance.-' The efficiency measure developed inthis study can be used for financial analysis because it is based on publiclyavailable information. The paper demonstrates the feasibility and usefulnessof this measure for financial analysis by analyzing the changes in efficiency offirms that have recently undergone a management buyout (MBO).'*

Results for a sample of MBO firms in the manufacturing sector show thatat one year post-MBO, the level of TFP is below the level one and two yearsbefore the MBO. The productivity gap seems to narrow in the second year.This latter result is similar to the results obtained by Lichtenberg and Siegel(1989), who found that for plants involved in changes in ownership, relativeproductivity exhibited a U-shaped curve and that significant gains fromchanges in ownership did not appear until a number of years after the event.But the results in this study are contrary to those obtained by Kaplan (1989)and Smith (1990), who each found large improvements in postbuyout perfor-mance immediately after the buyout.^

The paper is organized as follows. The next section defines TFP and therelationship between TFP and manager-specific factors. The third sectiondescribes the link between profits and productivity at the firm level and devel-ops a simple model for measuring relative changes in productivity. The follow-ing section specifies managerial performance in the context of a managementbuyout and briefly describes previous research on the topic. Research designand data are provided in the fifth section, and results and conclusions aregiven in the sixth section. Finally, computational details of TFP measurementare described in Appendix 1, and the biases due to the assumptions underlyingthe TFP model are discussed in appendices 2 and 3.

Total bctor productivityProductivity is defined as the efficiency with which inputs are used to provideoutputs. Output can be broadly defined as a function of quantity as well asproduct attributes such as quality, level of differentiation, and mode of deliveryto the customer. The corresponding definition of input includes all resources

Managerial Efficiency 181

used for activities such as production; selling, general, and administrative(SG&A); and research and development (R&D). Productivity as definedabove encompasses a broader concept than production efficiency alonebecause it represents the collective efficiency of all factor usage of the firm.Also, it is important to note that the TFP measure developed in this paperincorporates the concepts of "technical" and "allocative" efficiencies,^ each ofwhich is a valid component of a total measure of managerial efficiency.

Measurement of total factor productivityAll methods of measuring efficiencies are based on the assumption of anunderlying production function. Previous researchers such as Abramowitz(1956), Denison (1974), and Kendrick (1984) have used Laspeyers TFPindexes based on the constant elasticity of substitution (CES) productionfunction. Solow (1957), Christensen and Jorgenson (1970), and Hulten (1979)used TFP Divisia indexes, for which the implicit production function is a con-stant returns to scale translog function.

The TFP measure used in this study is based on the Leontief productionfunction, which assumes a generalized fixed-proportion production technol-ogy and is a special class of the CES production function.^ This measure issimilar to those used by Abramowitz (1956) and Kendrick (1984) and is a vari-ation of Farrell's measure of efficiency (Farrell 1957). More recently, thismeasure has been used by Cowling et al. (1985) to examine changes in effi-ciency due to mergers. This measure is also similar to the TFP measure devel-oped by the American Productivity Center (APC).

Due to the wide variety of productivity measures used in research, considerableeffort has been devoted to examining potential biases in productivity measures dueto different assumptions of the underlying production functions. Although it is dif-ficult to ascertain the exact specification of production function for a particularfirm, in theory certain functional forms of the production functions are consideredto have more desirable properties than others. But there is a lack of consensusabout the general "best" measure for productivity (Nelson 1981).

Managers, especially those at the higher levels of the organization, can exer-cise different options to pursue diverse goals. For example, managerial deci-sions can focus on more efficient use of inputs, use of cheaper inputs, or newproduction technology (i.e., resulting in a nonscaling change in the productionfunction or even a totally different production function). Although the firsttwo can be modeled with the assumption of fixed-factor and Cobb-Douglasproduction functions, respectively, the last option cannot be modeled with anyproduction function that assumes a constant returns production technology.Using the wrong model for measuring changes in efficiency results in biasedestimates of the reality. Because every production function is based on certainrestrictions, it is useful to examine the associated biases and to establish upperand/or lower bounds for them. The potential biases due to the assumption of afixed-factor proportion production function are examined in Appendix 2. Asdiscussed there, the TFP measure based on a fixed-factor production function

182 K. Verma

can include changes in technical as well as allocative efficiencies. Therefore, itis potentially a biased estimator of the changes in the technical efficiency of afirm. However, since top management is responsible for decisions affectingtechnical as well as allocative efficiencies, a comprehensive TFP measureincorporating both is a more appropriate measure for evaluating managerialperformance.

A model for relative changes in productivityThe foUowing model is based on the model derived by Cowling et al. (1985).In this model, an increase in TFP or efficiency, E, is defined as a reduction inthe ratio of inputs used to outputs produced. A direct method of measuringthis change would compare the list of inputs and outputs for a firm from yearto year and aggregate these changes according to a predetermined weightingscheme. Such detailed lists are not publicly available at the firm level. Butunder the assumption that either (1) the proportion in which inputs are usedor (2) the relative prices of inputs and outputs do not fiuctuate greatly fromyear to year, information about inputs and outputs can be deduced from therevenue, costs, and input and output prices.*

For notational simplicity, suppose a firm produces one type of output anduses N factors of production, i = 1,2, N. Let

R = Total revenue

and

C = Total costs

Then profit

-ir= R-C

Here

PQ = Price of a unit of output

Q = Number of units of output produced

q^ = Quantity of factor i consumed

Pj = Unit price of factor i consumed

Under the assumption that factors of production are consumed in fixed pro-portions to one another, the quantity of factor i consumed can be written as

(2)

where a-, is the relative quantity of factor i used to produce one unit of output.

Managerial Efficiency 183

and Jt is a measure of total factor requirement per unit of output. Of course,unit factor requirement, k, is the inverse of efficiency, E, as defined above.Because the a,, are relative quantities, computed measures of k are meaningfulonly for relative comparisons, not as absolute values. Substituting equation (2)in equation (1) gives

(3)

Now "SMJPI = PJ, is a definition of a fixed-weight price index of inputs LSubstituting Pjin equation (3) gives

or~ " IT

PjR

Therefore, TFP or efficiency E is given by

(4)

1 PjRTFP = E= = — - — (5)

k PoC

Introducing ( and t-^\ as time subscripts, changes in efficiency over a timeperiod are captured by the ratio

Here

—— = Changes in specific prices in the input market and

Po,t-t—̂ = Changes in specific prices in the output market

184 K. Verma

The relative change in TFP given by equation (6) is a product of two terms.The first term is the ratio of the relative change in output values to the relativechange in input values, and the second term is an index of specific prices giv-ing the relative change in unit cost and unit price over time. This formulationis a simple variation of the relationship between productivity and profitabilityin the APC performance measurement system (Kendrick 1984, 59, Banker etal. 1989). The preceding equation in the APC's terminology can be written as

Productivity = Profitability XPrice recovery

In this equation, productivity is the relative change in the relationshipbetween output and input quantities, profitability is the relative change in out-put and input values, and price recovery is an index of the ability of an organi-zation to pass on increases in costs through price changes.

The preceding model is operationalized using measures of price recovery atthe industry level.^ The use of a price recovery index computed from averageindustry prices adjusts firm profits for the uncontrollable component of pricechanges at the industry level.^"

To summarize, the TFP measure used in this study is based on one of themany models that have traditionally been used to study productivity. It incor-porates changes in technical and allocative efficiencies and the controllableportion of the price recovery (i.e., over and above the level achieved by indus-try). Computation of this measure using industry data separates profits intothe controllable (TFP) and uncontrollable (price recovery at the industrylevel) components and therefore gives direct measures that are meaningful forevaluating managerial performance. This measure is useful for financial analy-sis because it uses publicly available financial and price data instead of operat-ing data, which are difficult to access at the firm or the line-of-business level.

Management effectiveness and management buyoutsManagement buyouts are corporate restructuring in which a group ofinvestors, including some of the firm's management, purchases all of theequity of the firm. Usually these restructurings result in the MBO firm takingon large amounts of debt to finance the transaction.^^ It has been argued thatservicing the heavy debt burden severely limits managerial discretion aboutcurrent and future discretionary expenses such as R&D and other long-terminvestments.^^ Su^h restructurings, therefore, have the potential to harm firmperformance in the long term.

An alternative hypothesis argues that debt performs a valuable monitoringfunction by reducing financial losses from managers having excessive controlover the firm's resources. The argument continues that after an MBO, themanagers as part owners of the restructured firm have better aligned incen-tives and are thus motivated to make better managerial decisions (Jensen1987). A number of studies (Kaplan 1989, Smith 1990, and Lichtenberg and

Managerial Efficiency 185

Siegel 1989) have documented increases in postbuyout efficiency. These stud-ies are now briefiy described.

Kaplan (1989) examined post-MBO performance to evaluate gains fromthe buyout transaction. In this study, he compared prebuyout to postbuyoutfirm performance for 36 manufacturing and nonmanufacturing MBO firms.Using accounting measures such as sales revenue and based operating margin—{EBIT + Depredation) / Sales—to measure firm efficiency, Kaplan foundthat postbuyout sales one year after the buyout as compared to one yearbefore the buyout improve by 9.9 percent and 0.95 percent, respectively.When adjusted for industry, the changes in sales and operating margins are- 8 percent and +1.5 percent, respectively. From these and other similarresults, he concludes that operating improvements are an important source ofvalue in MBOs. Smith (1990) used a number of accounting measures to evalu-ate firm performance and came to a similar conclusion.

The accounting measures used in the Kaplan and Smith studies are aggre-gate measures of performance that do not directly separate the controllableand uncontrollable portions of managerial performance. Although this prob-lem can be mitigated by a carefully chosen industry control sample, unlessMBO firms operate predominantly in a single market, the usual practice ofmatching control firms based on the primary SIC code alone ignores thediversified nature of the firm. In the present sample, the share of the salesfrom the primary line of business ranges from a high of 90 percent to a low of25 percent. This problem of inaccurate matching of the control group is fur-ther exacerbated in the context of MBOs because the large-scale asset sell offsthat often accompany such transactions may substantially change the originalmix of businesses.^3 In the present study, the problem of diversified firms hasbeen explicitly dealt with by computing weighted TFP indices using data fromthe line of business disclosures in the firm's financial statements.

Lichtenberg and Siegel (1989) (hereafter LS) examined the economiceffects of leveraged buyouts (LBOs), using plant-level data collected by theU.S. Bureau of Census. Their performance measure is a variation of TFPbased on a Cobb-Douglas production function using labor, capital, and mate-rials for inputs. Changes in TFP are measured by estimating the residuals froma regression using time-series and cross-sectional pooled data. The resultsindicate that plants involved in LBOs have significantly higher rates of growthas compared to other plants in the same industry.

Although the TFP measure used by LS measures technical efficiency,which is appropriate for evaluating plant-level performance, the TFP measuredefined in this study encompasses a broader definition of total efficiency thatis more appropriate for analyzing firmwide and line-of-business efficiency.Further, the input capital values used in the LS study are obtained by using aperpetual inventory method that computes postbuyout values of capital as thesum of the prebuyout value of capital and the reported net capital expendi-tures (capital expenditures minus divestitures measured at current bookvalue) for the intervening period. This method of measuring postbuyout capi-

186 K. Verma

tal values only partially removes the effects of writing up capital assets.Because this method does not restate the postbuyout book value of divestedassets that may have been written up at the time of the buyout, it systemati-cally understates net capital expenditures and therefore understates postbuy-out capital stock values.^*

Econometrically, this is an errors-in-variable problem, which can bias esti-mated coefficients (Judge 1976, 514) and hence lead to biased measures ofchanges in TFP for the MBO firms. The present study corrects for the effectsof accounting write-up of capital assets by using detailed information fromMBO-related disclosures and from schedules for plant and equipment(Schedules V and VI) in the lOKs.

To summarize, earlier studies of MBO performance have used differentmeasures and research methods. Using footnote disclosures and segment data,the author made an attempt in this study to restate accounting numbers whenappropriate and to carefully match lines of business with industry. In addition,to provide comparisons with earlier studies, MBO performance is also ana-lyzed using two other measures.^' These alternative measures are (1) an oper-ating cash flow based measure similar to the measures used by Kaplan (1989)and Smith (1990) and (2) a TFP measure based on the Cobb-Douglas produc-tion function (TFPC). This second measure is a variation of the TFP measureused by LS (1989).

Research design and data sourcesThe research design involves using equation (5) to compute TFP indices for asample of MBO firms. This computation requires calculating input and outputweighted price indices Pj and PQ using prices p,. and p^ and a correspondingset of weights a;.

Computation of the input and output weighted price indicesWeighted price indices are computed for each firm's lines of business asdefined by the four-digit SIC code. These indices are computed using industrydata corresponding to the assigned SIC code. For a line of business, therequired weights for inputs, a^, are computed from a detailed listing of all com-modities from the input-output tables published by the U.S. Department ofCommerce, Bureau of Economic Analysis (1977).

Denoting the value of the commodity / used by an industry by 5,- and divid-ing it by the total value of all inputs gives the required weights for the inputseries, ai = 5,- / 25,.. The weighted price index of all inputs can then be calcu-lated as

where WPIj is the wholesale price index for the /'*' commodity. A simplenumerical example illustrating the computation of weighted price indices andTFP measures is given in Appendix 1. For a diversified firm, changes in TFP

Managerial Efficiency 187

are analyzed at the total firm level and the line-of-business (LOB) level. Atthe firm level, a single, comprehensive weighted price index is computed bycombining the weighted price indices for the individual lines of business. Thecomposite input price index for the whole firm is given by

P,(firm) =

Here C^ is the total cost and Pjj^ the weighted price index for the Lfi^ LOB.The output price index, PQ, for an undiversified firm is the published price

index corresponding to its industry classification. For a diversified firm, thecomposite weighted price index is computed as

Si?POL

Here /?^ is the sales revenue for the L"' LOB, and P ^ ^ is the correspondingoutput price index.^^

The required line-of-business revenue and cost data are available from theindustry segment information disclosed in the notes to annual financialreports. The price series for inputs and outputs are available at the four-digitSIC level from the annual supplement to the Producer Price Series publishedby the Bureau of Labor Statistics (BLS). Employment wage series for manu-facturing workers are published in the annual Economic Report of thePresident. The price series for advertising, which is a significant input for mostconsumer product industries, can be obtained from the McCann-Erickson costindices for national and local advertisers.

The revenue and cost data are available from the annual reports andprospectuses for subsequent issues of equity. Post-MBO performance dataneed to be corrected for the effects of the purchase method of accountingused to write up assets and goodwill at the time of the buyout. Excess depreci-ation and amortization due to the write-up would overstate operating costsand, hence, understate measured TFP. Therefore, these excess expenses weresubtracted from post-MBO operating costs. The corrections for the deprecia-tion and amortization expenses were estimated from MBO-related disclosuresin the financial statements and from the schedules for plant and equipmentand accumulated depreciation (Schedules V and VI) in the lOKs.

There were about 60 inputs for each industry. Commodity and serviceinputs that could not be matched with price data at the four-digit SIC levelwere priced using the average price series for all commodities and all services,respectively.

Computation of operating marginOperating margin is defined as the ratio of operating cash flow earnings

188 K. Verma

(EBIT -(- Depredation) to sales. This measure is similar to the efficiency mea-sures examined by Kaplan (1989) and Smith (1990).

Computation ofTFPCMeasurement of TFPC is based on the assumption of a constant returns toscale Cobb-Douglas production function. Growth in TFPC, denoted by TpPC,is given by TpPC = ^ - aK - (1 - a)L where ^ is the percent growth insales, K is the percent growth in capital, Z,is the percent growth in labor andmaterials, and a is the output elasticity with respect to capital, which can beapproximated by the share of capital in the sales of the firm (see Clark andGriliches 1984). This model is restricted to only two inputs because informa-tion about other inputs such as materials and energy is not disclosed sepa-rately in the financial statements.^^

Selection criteria for the sample of MBO firmsA preliminary sample of firms that underwent an MBO during the years 1982to 1986 was collected from information in the Investment Dealers Digest andthe Mergers and Acquisitions Digest}^ This original sample was subjected tothe following additional criteria.

1 The MBO firms had financial information for at least one year before andone year after the fiscal year of MBO occurrence. Since MBOs usuallyoccurred in the middle of a fiscal year, this criterion ensured that compari-son of firm performance encompassed full rather than partial years. Also,less than one full year of post-MBO data does not give meaningful infor-mation for evaluating the outcomes of managerial decisions. Post-MBOdata are not available for every firm because only firms with outstandingpublic debt, or new issues of equity, are required by the SEC to publishfinancial data. This criterion eliminated many firms that had publicly avail-able post-MBO financial data for less than one year.

2 Only those firms that could be matched at the four-digit SIC level withboth the producer price series and the industry input-output tables wereincluded in the sample. This constraint limited the analysis to the timeperiod 1983 to 1986 and to firms in the manufacturing sector.^^

3 Firms were included in the sample if the industry price series was completefor every output and for at least 75 percent of the value of the inputs. Thiscriterion minimized the use of price indices that were too aggregate.

A description of the final sample is given in Table 1.Before analyzing the relative productivity measures computed in this study,

it is useful to be aware of the potential sources of bias that might be present.Computation of TFP requires the use of firm-level accounting data and indus-try-level input and output price indices. The use of these data can bias resultsbecause of (1) the assumption of a fixed-factor proportion production technol-ogy, (2) the potential manipulation of accounting data by the managers of thebuyout firm (DeAngelo 1986), (3) the use of industry input-output tables pub-

Managerial Efficiency 189

lished in 1977, (4) the understatement of operating costs due to exclusion ofthe explicit cost of capital, and (5) the effect of capacity utilization on themeasurement of TFP. A detailed discussion of these potential biases is givenin appendices 2 and 3. In summary, these biases are expected to be small; nev-ertheless, they can understate or, as discussed in Appendix 3, more likelyoverstate post-MBO gains in productivity.

ResultsThe measures used to evaluate performance are percent changes in (1) TFP,(2) industry-adjusted TFP,^" (3) operating margin, and (4) TFPC

Denoting the year of the MBO as year 0, Table 2 includes the changes inthe four performance measures from one year before the MBO (year -1 ) toone year after the MBO (year +1). Of the original 17 firms in the sample. Dr.Pepper, with very large changes in all four performance measures, affected the

TABLE 1Description of the sample

Firms

AmstarBlue Bell

Dr. Pepper

Harte-HanksLeslie Fay

ToppsMary KayConairGuardianPannill KnittingSwiftNational Gypsum

Fruehof

Amsted

SFN

Palm Beadi

Papercraft

Primary SIC industry description

Code

2061-3231-8

2086

2711231-8

2067284436303211225420113270

3714

3530

2731

231-8

2640

Product

SugarApparel made frompurchased materialBottled and cannedsoft drinksNewspapersApparel made frompurchased materialChewing gumCosmeticsHousehold appliancesGlassKnitwear millsMeat productsConcrete, gypsum, andplasterTransportationequipmentPrimary metal machineryand equipmentBooks, publishing, andprintingApparel made fi'ompurchased materialPaper

MBO year

1984

1984

19841984

1983198319851985198519841986

1986

1986

1986

1985

19851985

190 K. Verma

statistical significance of all magnitude-based tests.^ This firm appeared to bea clear outlier because the inclusion of no other firm had the same impact.Furthermore, the fact that Dr. Pepper underwent two buyouts within twoyears made it difficult to separate the effects of the first buyout. For these rea-sons, the following results are based on a sample of 16 firms, excluding Dr.Pepper.

Percent changes in TFP range from -14.95 for SFN to +5.59 for Guardian,with the changes in industry-adjusted TFP ranging from -23.76 for SFN to+4.53 for Fruehof. For operating margin, the percent change ranges from

TABLE 2Percent change in total factor productivity (TFP), industry-adjusted TFP, operatingmargin at one year post-MBO (year +1), and TFP based on a Cobb-Douglas model,as compared to one year pre-MBO (year —1) using data at the firm level

Industry-adjusted OperatingFirms TFP TFP margin TFPC

0.46 4.672.55 3.29

-1.42 -1.662.36 2.775.76 -1.22

-5.60 -4.02-6.76 -8.21

2.50 1.79-1.82 -2.35

0.28 -0.50-2.71 -5.73-4.38 -2.65-3.82 -2.26-0.90 -0.19-4.24 -4.13-5.27 -5.10

TFP is computed using equation (5),whereR = Sales revenueC = Operating costs

AmstarBlueBeUHarte-HanksLeslie FayToppsMary KayConairGuardian

0.931.09

-3.60-0.17-8.89-9.40-2.47

5.59Pannill Knitting -2.11Swift -1.91National Gypsum —6.17FmehofAmstedSFNPalm BeachPapercraft

-2.86-5.09

-14.95-4.99-7.72

0.97-2.45-2.78-3.21

-10.53-17.32-6.64

1.52-4.95-0.85-8.67

4.53-10.98-23.76-5.07

-10.67

j Q = The weighted price input and output indices, respectively.Industiy-adjusted TFP is calculated as the difference between the percentage change infirm TFP and the percentage change in indtistry TFP.Operating margin is defined as the ratio of operating income, before subtracting depre-dation and interest, to sales.TFPC is the change in total factor productivity defitied assumitig a Cobb-Douglasproduction function. TFPC = Q - ak - {i — a)L, where Q is the percent growthin sales, K is the percent growth in capital, L is the percent change in labor plusmaterials, and or is capital's share in the sales for the period.

Managerial Efficiency 191

-6.76 for Conair to 4-5.76 for Topps. Changes in TFPC range from -8.21 forConair to -1-4.67 for Amstar. Because operating margin and TFPC are unad-justed for industry performance, it is meaningful to compare them with thesimilarly unadjusted TFP measure. The general range of the three perfor-mance measures is similar with a somewhat larger range for TFP. Althoughthere seems to be a preponderance of negative changes in all three measures,the rank ordering of the firms with the highest and the lowest measures is notconsistent.

Summary statistics from Table 2 are given in Table 3. The median andmean changes for each of the performance measures are negative. Significantlevels for the mean changes range from +0.01 to -J-0.20. Even though thedeclines in operating margin are not as statistically significant as the otherthree measures, these changes are different from the positive and significantchanges reported by Kaplan (1989). Because the operating margin is similar tothe measures defined by Kaplan (1989) and because the sample in this study isalmost identical to the manufacturing half of the sample in Kaplan's study,this difference in results can be explained only by assuming large increases inthe operating margins of nonmanufacturing firms. Intuitively, it is possiblethat nonmanufacturing firms can respond more quickly in the marketplaceand are therefore able to exhibit improved performance more quickly thanmanufacturing firms for which improvements take longer to implement. Forexample, although it might be possible for a retail firm to pare down invento-ries quickly, it takes longer to successfully implement programs such as just-in-time ones to reduce inventory levels for manufacturing firms.

To check this assertion, the author computed profit margins for a sample ofnonmanufacturing MBO firms. These results are presented in Table 4.Although this is a partial sample from Kaplan's study, the mean and medianimprovements in the postbuyout profit margins of the nonmanufacturing MBOfirms are -)-4.2 and +1.6 percent, respectively. More important, the results forthe combined sample of manufacturing and the nonmanufacturing firms showthat the mean and median improvements in postbuyout profit margins is +0.71

TABLE 3Comparison of total factor productivity (TFP), industry-adjusted TFP, and operatingmargin at one year post-MBO (year +1) to one year pre-MBO (year -1 ) using data atthe firm level

Percentage change in TFPPercentage change

in industry-adjusted TFPPercentage change in

[ operating marginPercentage change in TFPC

Sample size

16

16

1616

Median

-3.23

-5.01

-1.57-1.96

Mean

-3.92

-6.30

-1.44-1.59

Statistic

-3.15

-3.44

-1.69-1.82

Significancelevel

.01

.05

.20

.10

192 K. Verma

TABLE 4Percent changes in operating margins for nonmanufactudng MBO firms at one yearpost-MBO (year +1) as compared to one year pre-MBO (year -1) .

Finn Changes in operating margins

Empire Gas 0.11JackEckard 2.32ARA Holding Co. 0.50Atlas Van Lines 0.87Cole National 1.76Devon Group 11.6Edgecomb Distdbutor 0.22Levitz Furniture -4.57Storerl. 24.57Cedar Point 4.33Mean change 4.2 percentMedian change 1.6 percent

Operating margin is defined as the ratio of operating income before subtracting depre-ciation and interest, to sales.

percent and -1-0.24 percent, respectively. The results of a comparison of theseto the mean and median changes in the operating margin of -1.44 and -1.57(see Table 3) seem to indicate that the results in Kaplan (1989) may have beendriven by the large positive gains exhibited by the firms in the nonmanufactur-ing sector.

Table 5 gives the nonparametric analysis over the four comparison periods(i.e., from year - 1 to +1 , year - 1 to -1-2, year - 2 to +1, and year - 2 to +2).Nonparametric tests are more suitable for small sample sizes because they arefree from the restricting assumption of a particular distribution underlying theobservations. The first panel gives the results from the nonparametric signtest. For the change from year - 1 to +1 and from year - 2 to -1-1, each of theperformance measures is different from the null of no changes in performancelevel. Further, the preponderance of negative changes supports the conclusionof a decline in performance one year after the MBO. These results are statisti-cally significant at p values ranging from +0.008 for changes in industry-adjusted TFP to -1-0.20 for changes in operating margin.

The results from the magnitude-based Wilcoxon matched-pairs signed-ranks test are mixed. Here the negative changes in industry-adjusted TFP areuniformly significant for comparisons over each of the time intervals. But,although each of the other performance measures shows a significant decUnefor the comparison from year —1 to +1 , this result is not true for comparisonswith year -1-2. Together these results suggest that whereas the post-MBO per-formance may have improved individually for some of the firms in the sample,this improvement is less than the average improvement for the industry.

The analysis outlined here is repeated using TFP, industry-adjusted TFP,and operating margin measures computed from data at the line-of-business

Managerial Efficiency 193

TABLE5Comparison of total factor productivity fTFP), industry-adjusted TFP, operatingmargin, and TFP based on a Cobb-Douglas model, at the firm level,using Donparametric tests

Sign test

Percent change in TFP

Percent change inindustry-adjusted TFP

Percent change inoperating margin

Percent change in TFPC

Proportion of negative changes

Year(-1 to +1)

13/16(.02)-13/16(.02)10/16(.20)12/16(.07)

Wilcoxon matched-pair signed-ranks test

SigniGcance levelPercent change in TFPPercent change in

industry-adjusted TFPPercent change In

operating marginPercent change in TFPC

(.01)

(.01)

(.20)(.10)

Year(-1 to +2)

5/8(ns)6/8(.30)5/6(.20)5/6(.20)

(.02)

(.02)

(ns)(ns)

Year(-2to+l)

11/15(.10)13/15(.008)9/12(.15)10/12(.02)

(ns)

(.05)

(ns)(ns)

Year( -2 to +2)

4/8(ns)

7/8(07)5/6(.20)5/6(.20)

(ns)

(.01)

(ns)(ns)

Significance levels are in parentheses.

Ievel.22 The advantages of analysis at the line-of-business level are that (1)TFP is more accurately matched with the industry TFP and (2) an increase inthe number of observations increases the power of the statistical tests. One ofthe disadvantages is that this analysis could be biased because of a lack ofindependence between each of the observations. In other words, if the post-buyout performance of the diversified MBO firms is systematically higher(lower) than that of the undiversified firms, the data at the line-of-businesslevel would weight the results to overstate (understate) performance. Thispossibility is examined by separating the sample of lines of business into twosubsamples, one representing the lines of business from diversified firms andthe other from undiversified firms. Essentially similar results (not reported)from the two subsamples seem to indicate that a bias, if it exists, is small.

Table 6 gives the summary statistics for the comparisons from year - 1 to+1 at the line-of-business level. The mean and median changes for each of thevariables are once again negative. The mean changes in productivity measuresare statistically significant but the mean change in operating margin is not.These results are similar to the results obtained from analyzing firm-level data.

Nonparametric results in Table 7 are strongest for the negative changes in

194 K. Verma

industry-adjusted TFP. Similar to the results with firm-level data, the compar-isons from year —1 to -t-1 are uniformly negative and statistically significantfor the three performance measures. But industry-adjusted TFP is the onlymeasure that consistently shows statistically significant negative changes overeach of the time periods. Once again, this suggests that the slight improve-ments in MBO performance over time are still less than the average improve-ments for the industry.

Finally, the seemingly anomalous results based on TFP versus operating

TABLE 6Comparison of total factor productivity (TFP), industry-adjusted TFP, and operatingmargin at one year post-MBO (year -H) to one year pre-MBO (year —1) usingline-of-business data

Percent change in TFPPercent change in

industry-adjusted TFPPercent change in

operating margin

Sample size

24

24

22

Median

-4.06

-4.72

-0.90

Mean

-3.7

-6.4

-0.69

tStatistic

-4.06

-3.90

-0.73

Significancelevel

0.01

0.01

ns

TABLE 7Comparison of total factor productivity CTFP), industry-adjusted TFP, and operatingmargin at the line-of-business level using nonparametric tests

Sign test

Percent changein TFP

Percent change inindustry-adjusted TFP

Percent change inoperating margin

Proportion of negative changes

Year( -1 to +1)

21/24(.001)

20/24(.001)

15/24(.3)

Wilcoxon matched-pair signed-ranks test

Percent change in TFP

Percent change inindustry-adjusted TFP

Percent change inoperating margin

Year( -1 to +2)

6/10(ns)

8/10

(1)6/9(ns)

Significance level

(.01)

(.01)

(.2)

(ns)

(.05)

(ns)

Year(-2to+l)

12/20(ns)

16/20(.01)10/19(ns)

(ns)

(.01)

(ns)

Year( - 2 to+2)

5/10(ns)

7/8(.02)6/8(.25)

(ns)

(.01)

(ns)

Significance levels are in parentheses.

Managerial Efficiency 195

margins can be reconciled by separating the changes in profitability into theirproductivity and price recovery components, as given in Equation (6) for thetwo-year comparison, year —1 to +1 . Table 8 indicates, in the case of Amstar,that profitability showed a 16 percent increase, but productivity improved by

TABLE8Changes in profitability, productivity, and price recovery ratios from one yearpre-.MBO (year —1) to one year post-MBO (year +1)

Firm

AmstarBlue BellHark HanksLeslie FayToppsMaiy KayConairGuardianPannill KnittingSwiftNational GypsumFruehofAmstedSFNPalm BeachPapercraft

Profitability

1.0161.0251.0001.0291.0000.9260.9621.0240.9851.0051.0000.9630.9641.0060.9680.948

Price recovery

1.0071.0141.0361.0301.0971.0230.9870.9701.0061.0251.0660.9921.0161.1831.0191.027

Productivity

1.0091.0110.9640.9980.9110.9060.9751.0560.9790.9810.9380.9710.9490.8510.9500.923

Note: Values greater than 1 indicate relative increases; those less than 1 indicatedecreases.

A change in profitability is defined as the ratio

where ^(+i). ̂ {+iy ^(-l) ^^^ 9 - 1 ) ^^^ "'^ ̂ ^'^* revenue and operating costs at (year+1) and (year —1), respectively.A diange in price recovery is the ratio of the inflation factor in the output market tothe inflation factor in the input market.A diange in productivity is given by equation (6)

-(+1)

Here tj^and i/oare the inflation factors in the input and output markets, respectively.

196 K. Verma

only 9 percent. This discrepancy can be explained by examining the pricerecovery ratio, which shows that increases in industry output versus inputprices explain the remaining 7 percent increase in observed profitability.Similarly, Harte-Hanks' profitability remained unchanged, but its productivitydecreased by 3.6 percent. Once again, the price recovery ratio shows thatincreases in industry output prices outpaced increases in industry input pricesby the offsetting 3.6 percent. As data in the table indicate, a price recoveryratio greater than 1 inflates measures of profitability and, therefore, partiallyhides decreases in total factor productivity. That is, if a firm is unable to matchthe industry price recovery ratio and cannot compensate by being more effi-cient, TFP will show a decline. Therefore, the preceding analysis supports thetheory that to be a superior performer in an industry, a firm needs to imple-ment either cost leadership or successful differentiation strategy (Porter 1980,35).

Conclusions and extensionsBased on the data in this study, the results do not seem to support thehypothesis of enhanced post-MBO efficiency in the short term. Some evi-dence exists for an improvement in performance with time, but this evi-dence is not uniformly conclusive. The declines in efficiency are most con-vincing for the TFP and the industry-adjusted TFP, although othercomparative measures such as the operating margin and TFPC also showdeclines, not all of which are statistically significant. Nevertheless, theresults using any of the four measures are uniformly different from thelarge positive and statistically significant results reported in earlier studies.There are two possible explanatiotis for these discrepancies. There could besystematic differences between the magnitude and timing of improvementsin performance of (1) manufacturing and nonmanufacturing firms and (2)between the firm or line-of-business level and the plant level.^^ This impliesthat the hypothesis of improved managerial performance does not applyconsistently across all MBO firms but may depend on industry-, firm-, andplant-specific factors.

Anecdotal evidence in the press seems to support the findings in this study.After tits MBO, SFN has undergone liquidation; Amstar and Pannill Knittingare on credit watch; Palm Beach has defaulted on payment of preferred stockdividends; Papercraft, Harte-Hanks, and Freuhof are rumored to be in financialdifficulty; and several firms have had top management changes. Almost all thediversified and undiversified firms have engaged in large-scale asset sell-off pro-grams. Once again, determining whether these divestitures ultimately helped, orhurt, the long-term performance of these firms is an empirical question.

Finally, it should be noted that the performance measures evaluated over ashort period are less appropriate for measuring the effects of better-qualitydecisions for a firm. It can be argued that, especially for the firms in the manu-facturing sector, it takes more time for a firm to implement successful cost-minimizing or product-differentiation strategies. Also, the implementation of

Managerial Efficiency 197

such programs for the MBO firms could be slowed further because of thepressure to service MBO-related debt.̂ "* Therefore, due caution needs to beexercised before inferring long-term effects from short-term changes in per-formance.

This study has dealt with the development, feasibility, and usefulness of aperformance measure based on the total factor productivity of a firm to evalu-ate managerial performance. By focusing on the controllable portion of man-agerial performance, this measure provides a less noisy indicator of the qualityof managerial decision making. The method used in this study is based onpublicly available data and, hence, provides a way to supplement traditionalfinancial analysis for additional insights into the managerial stewardship func-tion. An extension of this research would be to examine the properties of TFPas a measure of the long-term performance of a firm. A study of its predictiveability with respect to subsequent changes in firm performance would help indeciding its usefulness as a measure of long-term success.

Appendix 1The TFP computation is illustrated with the following simplified example.Suppose firm A is in industry J, and at time t, industry J's input profileincludes four inputs: materials, labor, capital, and advertising. The data inputand financial data for the example are given in Table lA.

Table l AInput profie of industry J

Inputs

MatedalsLaborCapitalAdvertising

Total

Costs $

150210550120

1,030

Financial data for firm A

Sales 3.34 MMCosts 2.99 MM

Input coefficient

0.1460.2040.5340.116

LOO

Input pdce index(base pedod = t^)

1.400.951.200.90

Output pdce index (based pedod t^) = 1.308Then TFP is computed by first computing the weighted input pdce index for industry J.

Weighted input pdce index for industry J

PJ = 1.40 X 0.146 + 0.95 X 0.204 + 1.20 x 0.534 + 0.9 X 0.116 = 1.143

Then fi'om equation (5)

TFP = (3.34/2.99) X (1.143/1.308)= 0.977

198 K. Verma

Appendix 2Implications of assuming a fixed-factor production technologyThe assumption of a fixed-factor production technology implies that managersare restricted in decisions to substitute cheaper inputs for more expensiveinputs. Although assumptions of other production technologies such as theCobb-Douglas also preclude managerial actions that result in nonscalingchanges in production function, the fixed factor technology, could lead to evenmore biases by imposing a further restriction. The following discussion exam-ines these bias (see Cowling et al 1985).

Referring to Figure 1, IQ is the isoquant for a unit of output in the initialperiod and IC is the associated cost line. IQ' is the isoquant for the same out-put in the subsequent period. The slope of the line ODEF represents theassumed factor proportions, and the distance along that ray is proportional tothe cost of using the factors in those proportions. IQ' represents a more effi-cient process because the cost of producing the same output is smaller.Because k measures total costs for one unit of production and k=\ IE whereE is the technical efficiency, the relative value of total factor requirementsbefore and after the change is given by Ar̂ / ATJ (£3 / ^1) = OEIOD.

As long as the factors are consumed in fixed proportions, it is immaterial

Figure 1 Biases using fixed-factor production function

IQ

IQ-

\ IC

Factor i

Managerial Efficiency 199

whether the relative prices of factors move divergently. If factor proportionschange, however, as in the case when the isoquant is IQ", an element of bias willbe introduced in the measurement insofar that the gain in efficiency will beaffected by the relative factor prices (this problem arises whenever index num-bers are used).

One reason for factor proportions to change is that a change in factorprices might induce the substitution of one factor for another. In this case, theisoquants are better represented by curves rather than right angles. This caseis illustrated in Figure 2. Assuming the fixed proportions are represented byline OF, the calculated value of the factor requirements before and after thechange k^ I fcj (^2^ ^ l ) ^ ^ ' correspond to OE/ OB. True technical efficiencydifferences would be captured by OE / OD. If this is regarded as the "true"ratio of factor requirements, then k-^ / ^2 will overestimate the savings in factorrequirements by the ratio OD/OB. This bias measures changes in the alloca-tive efficiency of the firm. In other words, although OE / OB measureschanges in technical efficiency with a bias, it is a total efficiency measure incor-porating changes in both technical and allocative efficiencies.

Because top managers are responsible for all decisions, including decisionsabout the use of cheaper inputs, both technical and allocative efficiencies

Figure 2 Substitution of factors because of change in input prices

IQ'

Factor i

200 K. Verma

should be considered valid components of a measure of managerial perfor-mance. Therefore observed changes in the total efficiency are the relevantmeasures for evaluating top management performance.

Appendix 3Biases due to the use of accounting dataMeasurement of TFP using accounting data can lead to biased measures ofefficiency for two reasons. The first is due to the incentives managers have tounderstate prebuyout accounting profit indicators in order to understate firmvalue. Results from previous research about the existence and relative impor-tance of this bias are mixed. According to DeAngelo, this bias should be small(see DeAngelo 1986), but Wu (1990) found evidence of earnings manipulationprior to MBO offers. Nevertheless, systematic understatement of prebuyoutperformance numbers would result in overstated measures of relativeimprovements in the postbuyout firm performance.

The second reason for biased measures of performance using accountingmeasures is that accounting profits overstate economic profits by the "interest"component of the cost of all inputs. For example, the portion of assets financedby debt shows an explicit interest charge, and dividends on equity are only apart of the implicit interest charge. In this study, the measure of profits does notinclude the explicit interest charge or dividends because (1) as just mentioned,these are only partial measures of total interest cost and (2) efficiency in thisstudy is not defined to include the effects of a firm's financing or dividend poli-cies. The bias due to this exclusion is small if the proportion of interest in totalcosts does not vary over time. This condition may not hold for MBOs becausethe higher leverage inherent in the buyout transaction is expected to increasethe riskiness and hence the postbuyout cost of capital for the firm. Therefore, ifthis input is substituted for other inputs such as materials or equipment, exclu-sion of this from the profit measure would overstate the TFP and TFPC

When accounting segment data are used, there can a potential problemthat certain costs, such as those for information systems or R&D, are central-ized and are subsequently allocated to the segments based on an arbitraryallocation scheme. Because information about the magnitude of commoncosts and their allocation schemes is not disclosed in the financial statements,it is difficult to know whether they are appropriately allocated. Therefore, tooperationalize the study one has to assume that any mismeasurement of costsdue to inappropriate allocation of common costs is small. If this were not true,the analysis based on individual lines of business could be biased.

The use of industry-level input-output tables, the most recent of which waspublished in 1977, can lead to biased measures for TFP. The size of this biaswould be small if over the test period (year - 2 to year +2), (1) the factor pro-portions did not vary or (2) the relative prices of factors stayed constant.Cowling et al. (1985, 64) tested for these conditions for a sample of mergerfirms and did not find very large fluctuations in either factor proportions orrelative prices over the few years of comparison.

Managerial Efficiency 201

Finally, the measure for TFP may vary due to changes in the degree of uti-lization of the productive capacity. To the extent that this is a reflection of thefirm's efficiency in production planning, it is reasonable that TFP shouldincorporate it. On the other hand, the degree of capacity utilization can bedue to large shifts in demand through the trade cycle and may be uncontrol-lable at the firm level. The effect of this bias can be controlled by comparisonswith the industry.

In summary, the biases mentioned above are expected to be small but havethe potential of understating or more likely overstating the real gains in pro-ductivity.

Endnotes1 Tlie controllable and uncontrollable portions of managerial performance have also

been labeled as "systematic" and "unsystematic" (see Antle and Smith 1986 for thedevelopment of an indirect measure of the unsystematic profits of a firm). Based onthe agency theory of relative performance evaluation, the systematic (unsystematic)profits are defined as the profits that can (cannot) be explained by industrywidefiuctuations in profitability. TFP, as defined in the study, is a direct measure of theunsystematic portion of the profits of the firm.

2 The total factor productivity index can be broadly defined as a ratio of an aggregateof outputs to an aggregate of inputs. By contrast, partial productivity indexes areratios of aggregate output to some inputs only.

3 For more informative finandal statement analysis, sitnilar measures of efficiency thatcontrol for changes in input and output prices have been proposed by Hawkins (1989).

4 It is important to note here that this study tests the joint hypothesis of (1) validityusing TFP to measure managerial performance and (2) evaluating the impact ofMBOs. This endeavor is similar to the tradition in finance literature of testing thejoint hypotheses of market efficiency and some other economic phenomenon beingstudied.

5 For the sample of manufacturing firms in this study, operating margin, one of theefficiency measures used by Kaplan (1989), does not exhibit the large postbuyoutincreases documented by him. This result seems to indicate that the large gains inperformance documented by Kaplan may be due to postbuyout performance of thenonmanufactudng firms in his sample (about 50 percent of the firms in his samplewere from the retail and service sectors).

6 Technical efficiency arises from more efficient production, either when there isincreased output from the same input or when there is the same output fromdecreased inputs. Allocative efficiency arises from efficient substitution of cheaperinputs. Traditionally, economists have defined productivity to mean technical effi-ciency.

7 Earlier studies examining efficiency have specifically focused on technical efficiencyalone. A TFP measure based on the assumption of a linear fixed-factor productionfunction measures technical and allocative efficiencies together, and it is not possi-ble to separate the effects due to more efficient use of inputs from better substitu-tion of cheaper inputs. However, because both of these activities refiect outcomes ofmanagerial decisions, they are valid components of a total measure of managerialefficiency (see Appendix 2 for a detailed discussion of these points).

8 See Appendix 2 for a discussion of the biases if these conditions are not true.The pdce recovery in the APC model is measured at the firm level. The formulationusing industry level price indices is necessary here because the firm-level price dataare not publicly available.

202 K. Verma

9 The pdce recovery in the APC model is measured at the firm level. The formula-tion using industry level pdce indices is necessary here because the firm level pdcedata is not publicly available.

10 Equation (6) is eauivalent to

^/(industry) ^i

^^* V. ''/(firm) '''/(industry) <̂ '

''o (industry) ''/(Hrm) ^ / - l

%(nrm) ^t-l

The dght-hand side gives the form of TFP estimated using measures of pdce recov-ery at the industry level. This formulation of TFP consists of two ratios, the firstgives the relative change in input and output quantities over time and the secondgives the relative change in pdce recovery over and above that at the industry level.These are the controllable portions of the firm performance.

11 For the sample of firms in this study, increase in debt to equity ratio at one yearafter the buyout as compared to one year before ranged from 37 percent to 332 per-cent. The median increase in the debt to equity ration was 90 percent.

12 See A.L. Cowan (1988, F5).13 For example, in the present study, two firms had to be eliminated from the sample

because their main SIC code after the buyout changed enough to change their statusfi'om manufactudng to service sector firms.

14 This point is best illustrated with a numedcal example. Suppose a buyout firm hadtwo buildings each with book value of $500 that was wdtten up to S1,(XX) after thebuyout. Suppose in the pedod after the buyout, the firm did not invest in any morecapital assets and divested one of the buildings. Then according to the formula usedbyLS

Postbuyout capital = Prebuyout capital + (Capital expenditures - Divestituresmeasured at the post buyout values ) = 1,000 + (0 - 1,000) = 0

whereas the true postbuyout value without the effects of the wdte-ups should havebeen $500.

15 Because the scope of this study does not include complete replications of earlierstudies on management buyouts, only two of the measures firom these studies arecomputed to assess whether there are differences in observed results across the dif-ferent measures. Because the results using each of three are mostly consistent, itappears that the different biases due to the use of TFP measures are not largeenough to give markedly different results from those obtained using other measures.

16 It is necessary to assume here that the defined lines of business are independentenough to allow linear aggregation. This assumption is borne out by the observationthat on average, the MBO firms had very small intersegment sales (less than 5 per-cent).

17 Here the output elasticities of labor and matedal inputs are assumed to be equal.This model differs from the model in LS (1989), where labor and matedal inputswere incorporated separately in the production function. In this study, however, it isnecessary to assume a reduced form of the production function because input valuesof matedals are not disclosed in the financial statements.

18 There is no significant dustedng of buyout firms in any of the industdes representedin the sample; see Table 1.

19 As of 1982, output indices have been published by the BLS for 114 industdes and3,450 input commodities. The BLS has been expanding its operation to report datafor all 493 industdes in the manufactudng and mining sector. Eventually, data will

Managerial Efficiency 203

also be available for the service sector. This will allow TFP analysis for firms inmany more industries than is now feasible.

20 Industry-adjusted TFP is calculated as the difference between percentage changes infirm TFP and the percentage changes in industry TFP published by the AmericanProductivity Center.

21 Changes in TFP, adjusted TFP, operating margin, and TFPC are +14.78, +14.70,+64.46, and +20.09, respectively. Inclusion of Dr. Pepper in the sample shifted thevalue of median percentage change in TFP from -3.23 to -3.20, and the signifi-cance level from +0.01 to +0.05.

22 It is not possible to compute TFPC at the line-of-business level because values ofcapital goods are not disclosed at this level.

23 While the first assertion has been tested and found to be true for a small sample ofnonmanufacturing firms, the scope of this study does not include explidt testing forthe differences between firm- and plant-level results.

24 The scope of this study did not extend to examining in detail the direct effects ofincreases in debt with changes in post-MBO performance. However, a simple exam-ination of the correlation between changes in TFP and TFPC with increases in debtlevels gives weakly significant results supporting the hypothesis that firms withsmaller increases in debt to equity ratios exhibit a somewhat superior performance.

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