Assessing the Impact of Management Buyouts on Economic Efficiency ...

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Department of Economics, Rensselaer Polytechnic Institute, 110 8 Street, Troy, NY, 12180-3590, USA. Tel: +1518-276-6387; Fax: +1-518-276-2235; URL: http://www.rpi.edu/dept/economics/; E-Mail: [email protected]

Assessing the Impact of Management Buyouts on Economic Efficiency: Plant-Level Evidence from the United Kingdom Richard Harris University of Durham Donald Siegel Rensselaer Polytechnic Institute Mike Wright University of Nottingham

Number 0304 October 2003

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Assessing the Impact of Management Buyouts on Economic Efficiency: Plant-Level Evidence from the United Kingdom Richard Harris Professor of Economics Department of Economics and Finance University of Durham Durham DH1 3HY United Kingdom Tel: +44 (0) 191 374 7280 Fax: +44 (0) 191 374 7289 [email protected] Donald S. Siegel* Professor of Economics Department of Economics Sage Building-Room 3502 Rensselaer Polytechnic Institute th 110 8 Street Troy, NY 12180-3590 United States Tel: (518) 276-2049 Fax: (518) 276-2035 [email protected] Mike Wright Professor of Financial Studies Nottingham University Business School Nottingham NG8 1BB United Kingdom Tel: +44 (0) 115 951 5257 Fax: +44 (0) 115 951 5204 [email protected]

We thank Boyan Jovanovic, Catherine Morrison Paul, and participants at the June 2002 North American Productivity Workshop at Union College for comments and suggestions.

* contact author

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Assessing the Impact of Management Buyouts on Economic Efficiency: Plant-Level Evidence from the United Kingdom

Abstract We assess the total factor productivity (TFP) of plants before and after management buyouts (MBOs), using the Annual Business Inquiry Respondents Database (ARD) file, which contains longitudinal data for approximately 36,000 U.K. manufacturing establishments. Our preliminary results suggest that these MBO establishments were less productive than comparable plants before the transfer of ownership. We also find that MBO plants experienced a substantial increase in productivity after a buyout. These post-buyout productivity gains appear to be pervasive across industries. The results imply that the improvement in economic performance may be due to measures undertaken by new owners or managers to reduce the labor intensity of production, through the outsourcing of intermediate goods and materials. Our evidence suggests that MBOs may be a useful mechanism for reducing agency costs and enhancing economic efficiency. More generally, the findings are consistent with recent theoretical and empirical evidence (Jovanovic and Rousseau (2002a, 2002b)) suggesting that takeovers result in the reallocation of a firm’s resources to more efficient uses and to better managers.

Keywords: Management Buyouts (MBOs), Total Factor Productivity (TFP), Annual Business Inquiry Respondents Database (ARD) File, Agency Theory

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I. INTRODUCTION During the 1990s, there was a resurgence in management buyouts (MBOs) in the United Kingdom, with the volume of assets transferred through these transactions rising from approximately £3 billion (approx. $4.7 billion) in 1990 to almost £17 billion (approx. $26.4 billion) in 1999. In an MBO, senior managers purchase a controlling interest in a firm’s common stock, typically using funds borrowed against the firm’s physical assets or cash flows as collateral to finance taking the firm private. Divisional buyouts and buyouts of privately held firms are also quite common. A critical policy issue concerning MBOs is whether they enhance economic efficiency. Agency theory suggests that the change in monitoring environment and ownership structure which accompanies an MBO should result in an improvement in economic performance. The standard empirical approaches to addressing this question are to analyze the impact of buyouts on stock prices or accounting profits (see Kaplan (1989) and Jensen (1993)).

These methods

are problematic for several reasons. First, many economists are increasingly skeptical about the “efficient markets” hypothesis (see Shleifer (2001)), which asserts that changes in share prices following announcements of buyouts reflect changes in future real performance or economic efficiency. Second, financial and accounting measures of performance are not necessarily perfectly correlated with real performance. It is also important to note that policy decisions regarding the optimal level of buyout activity hinge mainly on their impact on economic efficiency (i.e., the “social” returns to buyouts), not on their effects on share prices or profitability (i.e., the “private” returns to buyouts). Finally, most buyouts involve privatelyheld companies or divisions of larger, publicly-held firms, which precludes an analysis of stock

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prices or accounting profits. Thus, analyses of buyouts based on publicly-available datasets (e.g., Compustat or Datastream) could generate misleading estimates of the private or social returns to buyout activity. In an effort to overcome these limitations, Lichtenberg and Siegel (1990a), henceforth, LS, asserted that a more desirable methodological approach is to assess the total factor productivity (TFP) of plants before and after MBOs. Based on an analysis of the U.S. Census Bureau's Longitudinal Research Database (LRD), which contained data on more than 19,000 mostly large U.S. manufacturing plants for the years 1972-1988, the authors found that MBO plants had higher TFP than representative establishments in the same industry before they changed owners. However, they also reported that MBO plants experienced significant improvements in TFP after the MBO. More importantly, the authors also found that this enhancement in economic performance could not be attributed to reductions in R&D or capital investment, wage reductions, or layoffs of blue-collar personnel. The purpose of this study is to extend this type of productivity analysis in three important ways. First, we analyze a much more comprehensive database, containing virtually the entire population of MBOs in the U.K. As a result, our final sample of manufacturing plants is considerably larger and more representative than the extract of the LRD analyzed by LS (1990). This file provides us with sufficient degrees of freedom to assess whether the productivity impact of MBOs differs across sectors. Second, we employ more sophisticated econometric techniques and a broader set of control variables than those used in the previous study, in order to more effectively isolate the impact of MBOs on productivity. Finally, we also have much more recent data, as our buyouts occurred during the years 1994-1998.

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The remainder of this paper is organized as follows. Section II briefly outlines an agency theory perspective on MBOs that has direct implications for how buyouts affect economic performance. Section III contains a short review of trends in U.K. MBOs and discusses some previous studies of their impact on economic performance. This discussion serves to highlight the importance of plant-level, productivity analysis. The following section outlines the econometric methodology. Section V describes the data. Section VI presents empirical results. The final section contains preliminary conclusions and suggestions for additional research.

II. AN AGENCY THEORY PERSPECTIVE ON MANAGEMENT BUYOUTS We adopt an agency theory perspective on MBOs, which has direct implications for how buyouts affect productivity. That is because buyouts typically result in a change in the incentive and governance structure of the firm. In publicly-traded corporations, peak tier agency problems may arise where management hold negligible amounts of equity and diffuse shareholders are unable to exert effective monitoring. A second tier agency problem arises in divisions of large public corporations. Incomplete labor contracts in internal capital markets raise the possibility of opportunism and a resultant need for monitoring. This monitoring may be ineffective when remuneration of senior management is not linked to performance and access to divisional information may be problematical in large complex organizations. Difficulties in introducing appropriate performance-related incentives at the level of individual divisions may compound these problems (Thompson and Wright, 1995). Following an MBO, senior management typically obtains a significant share of the equity, with a small group of private equity investors garnering the balance. The subscribers of this

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private equity, whether a leveraged buyout association or a private equity firm, provide close monitoring of the bought out firm through requirements for detailed information, board representation, etc. (Jensen, 1993). Taking on significant debt to fund the purchase of the company introduces a major commitment to service this financing because of the threat of bankruptcy should interest payments not be met. Debt providers also set and monitor a range of covenants as a condition of extending loan finance (Citron, Robbie and Wright, 1997). These changes in incentive and governance mechanisms are expected to lead managers to seek out more efficient uses of factors of production.

III. REVIEW AND CRITIQUE OF EVIDENCE ON U.K. MANAGEMENT BUYOUTS As shown in Figure 1, there was significant growth in the value of assets transferred through buyouts in the U.K. during the 1980s, reaching a peak in 1989. Following a decline in 1990, MBO activity increased virtually monotonically until 2000. Figure 2 indicates that buyouts constituted approximately 50% of all U.K. takeovers over the period 1987-2001. These trends are quite similar to patterns of MBO activity in the U.S, with the exception that MBOs account for a larger fraction of overall takeover activity in the U.K. than in the U.S. Statistics on various types of U.K. MBOS are presented in Tables 1 and 2, based on the number and market value of transactions, respectively. Two key stylized facts emerge from these tables. The first is that an overwhelming majority of MBOs occur below the firm level, with the typical deal representing the divestment of a subsidiary of a large corporation or a transaction that affects only a few plants. The figures also reveal that most buyouts involve companies (or parts of companies) that are privately-held. We observe from these tables that

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full-firm MBOs involving publicly-traded companies constitute only a very small percentage of aggregate buyouts activity.1 These stylized facts underscore the difficulties of gathering pre and post-buyout performance data. Two concerns are that there is little publicly-available data on divisions and even when this information is present, it is limited to crude measures of profitability. Furthermore, there are no publicly available data on individual plants. Note also that the entities involved in these transactions are privately-held after the MBO occurs, even in the case of a fullfirm MBO involving a publicly-traded company.2 Interestingly, it is actually easier to collect information on the characteristics of privatelyheld firms in the U.K., as opposed to the U.S., due to the existence of the FAME and ONESOURCE UK Private+ databases. Both of these files contain similar company-level data from financial statements, as one would find in COMPUSTAT, or the British equivalent of COMPUSTAT called DATASTREAM. Using this file, Wright, Wilson, and Robbie (1996) examined the impact of full firm MBOs on accounting profits and concluded that firms experiencing an MBO generated significantly higher increases in return on assets than comparable firms that did not experience an MBO. A forthcoming study by Amess (2002) presents U.K. evidence on the effects of full-firm MBOs on TFP, based on ONESOURCE company-level data. We assert that it is inappropriate to estimate TFP using (publicly available) firm-level data for two reasons. First, the construction of TFP measures requires reliable and comprehensive information on capital and intermediate materials. These variables are typically not reported in financial statements and thus, are not 1 2

This is also the case in the U.S. Recall from Tables 1 and 2 that full-firm MBOs involving publicly-traded companies constitute only a very

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contained in COMPUSTAT, DATASTREAM, or ONESOURCE files. Second, the accuracy of TFP measures also depends on the accuracy of input and output price deflators, since inputs and output must be computed in constant dollars. The problem is that many large firms have plants in diverse industries, with substantial variation in price changes. However, they must be classified, at the corporate level, into a single 4-digit SIC industry. As demonstrated in Lichtenberg and Siegel (1991), the use of a single set of output and input deflators to construct estimates of the real output and inputs of firms can introduce a substantial amount of measurement error into the calculation of TFP measures. And finally, as alluded to in the previous section, much MBO activity occurs below the firm level. Thus, it is best to use plantlevel data to examine the impact of MBOs on economic performance.

IV. ECONOMETRIC MODEL The first and only plant-level study of the relationship between MBOs and TFP was LS (1990). In this paper, the authors employed a two-stage approach to assess the impact of MBOs on TFP. In the first stage, they computed residuals from within-industry (4-digit SIC) OLS regressions of Cobb-Douglas production functions of the following form (with error terms suppressed):

(1)

K Σ ln Yi = _k ln Xki k=1

where Y denotes output, X represents a vector of k inputs, and i refers to a plant. The second stage equation was:

small percentage of aggregate MBO activity.

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(2)

RELPRODi, t+m = f (MBOit)

where RELPROD is the productivity residual of plant i in year t + m; MBO is a dummy variable that equals 1 if the plant was involved in a management buyout; 0 otherwise. In contrast, our approach is based on GMM estimation of within industry (2-digit SIC), one-stage augmented Cobb-Douglas production functions. A one stage estimation procedure provides more efficient econometric estimates of the conventional arguments of the production function and other determinants of productivity (e.g. a set of MBO dummy variables) than the two-stage approach. We estimate variants of the following equation: (3) ln (OUTPUTit)=β0+β1ln(CAPITALit)+β2ln(LABORit) +β3ln(MATERIALSit)+ β4 PREMBOit +β5POSTMBOit +β6ln(AGEit)+β7AAit +β8 t +β9USit+β10EUit+β11OFOit 18 11 + Σ _j SICij + Σ γ k REGik + ait j=1

k=1

where OUTPUT is real gross-output; CAPITAL refers to plant and machinery capital stock3 , LABOR is total employment4 , MATERIALS is real intermediate inputs, PREMBO is a dummy variable that equals 1 if the plant was involved in a management buy-out during 1994-1998, POSTMBO is a dummy variable that equals 1 in each year after a buy-out occurs, AGE is the number of years the plant has been in operation5; AA is a dummy variable that equals 1 if the plant is located in an “Assisted Area” of Great Britain; t is a time trend; US, EU and OFO are dummy variables taken on a value of 1 if the plant is owned by a U.S., European Union, or other 3

See Harris and Drinkwater (2000) for a discussion of how capital stock measures are constructed using the ARD. Unfortunately, data on hours worked is not available 5 Plant age is only available from 1970; thus plants in existence before 1970 are coded as if they started operations in 1970. Note, this variable and the ownership, industry and region dummies were included as control variables since it is known that there is likely to be a relationship between productivity and such characteristics. We wish to 4

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foreign parent company; SIC is a dummy variable for the two-digit SIC industry of the plant (1980 SIC); REG is a dummy variable that is equal to if the plant is located in specific standard U.K. region; and ait is an error term. This error term is assumed to consist of three components: (4)

ait = ηi + ϑt + eit

with ηi affecting all observations for cross-section unit i; ϑt affecting all units for time period t; and eit affecting only unit i during period t. We assume that eit is serially correlated such that: (5)

eu = _eu-1 + uit

where uit is uncorrelated with any other part of the model, and ρ < 1 . As shown in Blundell and Bond (1999), if we invoke these assumptions, then equation (3) can be transformed into a dynamic form involving first-order lags of the variables and a well behaved error term. The inclusion of the following control variables in the production function: age, foreign ownership, assisted area, and regional dummies, is critical, given recent empirical evidence, based on the ARD, indicating these factors explain significant variation in TFP across plants (see Griffith (1999) and Harris (2002). Given that our final sample consists of 35,752 plants, we have sufficient degrees of freedom to include a full set of industry (2-digit SIC) dummies and also to interact the industry dummies with the conventional inputs in the production (capital, labor, and materials) and the MBO dummies. Thus, we have essentially estimated separate pre and post-buyout production functionS for each industry. Equation (3), or its dynamic counterpart, can be estimated using the Arellano and Bond (1998) General Method of Moments (GMM) systems approach available in PcGive (Dynamic net out these effects in order to concentrate on the relationship between productivity and management buy-outs.

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Panel Data Analysis), since this is sufficiently flexible to allow for both endogeneity of inputs (through the use of appropriate instruments) and a first-order autoregressive error term. We use the GMM systems approach to estimate the model in levels and first-differences. This is important, since Blundell and Bond (1999) argue that including both lagged levels and lagged firstdifferenced instruments leads to significant reductions in finite sample bias as a result of exploiting the additional moment conditions inherent from taking their system approach. All data need also to be weighted to ensure that the samples are representative of the population of U.K. manufacturing plants under consideration.6

V. DATA In order to assess the impact of MBOs on the productivity of U.K. manufacturing plants, we merged two files. The first is the Annual Respondents Database (ARD), constructed by the U.K. Office for National Statistics, consisting of individual establishment records from the Annual Census of Production.7 The ARD file contains detailed data on output, materials, energy, employment, and numerous plant and firm characteristics. This information can be used to construct measures of TFP, based on estimation of plant-level production functions. Griffith (1999) and Harris (2002) provide overviews of the ARD database. Each year, information is collected from approximately 14,000-19,000 establishments (known as “reporting units”), based on a sampling methodology that is biased towards larger establishments. However, 6

Note the data had to be weighted prior to use in PcGive10 (and thus any automatically generated constant terms were suppressed). 7 The U.S. counterpart of the ARD, the LRD, has been analyzed fairly extensively in recent years. Reviews of LRD-based studies are presented in Caves (1998) and Bartelsman and Doms (2000). An early version of the LRD, the Longitudinal Establishment Database (LED), was analyzed by Lichtenberg and Siegel (1987, 1990a, 1990b and 1991) and is described in Siegel (1988).

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it is important to note that the ARD file contains a higher proportion of smaller plants than the version of the LRD examined by L&S. More importantly, the ARD is an unbalanced plant-level panel data, in contrast to the extract of the LRD used by L&S, which was (primarily) a balanced panel. The unbalanced nature of the ARD is a critical feature of this database, since there is considerable entry and exit over a long sample period such as ours (1982-1998). Indeed, our final sample consists of 35,752 establishments. The second file we worked with contains comprehensive data on the characteristics of U.K. management buyouts from the Center for Management Buyout Research (CMBOR) at the Nottingham University Business School. The CMBOR data set is essentially the complete population of U.K. management buyouts, since the early 1980s. The database has no minimum size cut-off. The data are compiled from various sources. The primary source of information is a twice-yearly survey of all private equity and debt providers in the buy-out market.8 Other data sources include press releases, company reports and stock exchange circulars. This approach enables extensive cross-checking of data to ensure reliability while assurances that data provided will be treated confidentially enable details of transaction values and financing structures, which are often not made public in press releases, to be obtained. According to the latest quarterly report of the CMBOR, there were 1,564 buy-outs (of firms, divisions, or individual plants) in the U.K. during 1994-1998, approximately half involving manufacturing firms (CMBOR, 2002). We linked the ARD and CMBOR files, by matching names, addresses, and post-codes from the CMBOR file to comparable information in the ARD from the ONS inter-departmental business register (IDBR). It appears that we matched at least one plant for almost the complete

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This survey yields and effective 100 per cent response rate due to CMBOR’s reputation for confidentiality and the

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universe of MBOs in manufacturing, since were able to locate 979 of 1564 buyouts (62.6%) in the ARD (using the IDBR). This high rate of matching implies that there must be numerous firms that are classified in services at the corporate level (because their primary industry in the service sector), yet have manufacturing establishments. The end result is that we have a much larger and more representative sample than L&S. In the L&S study, the authors identified 48 MBOs and 399 plants. The corresponding numbers for our final sample are 979 MBOs and 4877 plants that experienced an MBO during the years 1994-1998. Our final sample is more representative of the universe of manufacturing plant and also the universe of MBOs. Another improvement is that we have an unbalanced panel of establishments, so our results are no based on a censored sample. Table 3 presents mean values of variables used in the plant-level production function analysis. It appears that MBO plants produce less output and consume less capital than nonMBO plants. In the following section, we present our econometric findings.

VI. EMPIRICAL RESULTS Table 4 reports econometric estimates of the parameters of Equation (3). Given that the model contains 148 parameters, we report only those coefficients that are statistically significant. Two sets of results are presented: short-run and long-run estimates, where the long-run estimates were derived from the short-run, dynamic results. The “short run” regressions include lagged values of OUTPUT, LABOR, MATERIALS, and CAPITAL as regressors. The diagnostic tests reveal that the model appears to be well specified, i.e., we appear to have a first order autoregressive error term and an appropriate set of instruments. Recall that our model assumes incentive to respondents of the provision of a complementary review of trends.

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that there should be negative first order serial correlation of differenced residuals, but an absence of second order serial correlation. Note also that the instrument set appears to be valid, since the Sargan (χ2) test of over-identifying restrictions does not allow us to reject the null hypothesis 9

that the instrument set is valid.

The parameter estimates suggest that there are constant returns to scale in U.K. manufacturing, as the estimated elasticities of output with respect to capital, labor, and materials sum to 1.03. Consistent with recent U.K. evidence (Harris 2002)), we find that foreign-owned establishments tend to be more productive than representative plants, while older plants and those in “assisted areas” tend to be less productive. We now turn our attention to coefficients on the pre and post-buyout dummy variables. In contrast to LS, we find that, on average, plants involved in MBOs were less productive (-1.6% and -2.0% less efficient in the short and long run, respectively) than other plants in the same industry before experiencing a buyout. However, consistent with LS, it appears that MBO plants experience a substantial increase in productivity after an MBO (+70.5% and +90.3% more efficient in the short and long run, respectively). These productivity gains are even higher than those reported in the U.S. More importantly, these findings (lower pre-MBO TFP and substantially higher post-MBO TFP) appear to be pervasive across sectors, as the average manufacturing plant experienced a substantial improvement in TFP in 14 out of 18 SIC industries. The only industries where MBOs did not result in an improvement in TFP were Non-Metallic Minerals (SIC 24), Chemicals (SIC 25), Electrical Engineering (SIC 34), and Alcohol and Tobacco (SIC 42). Note that unlike LS, these results are obtained using G M M

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See Arellano and Bond (1991) for additional information on these tests.

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estimation and after controlling for region, plant vintage, and foreign ownership. In an effort to “explain” the underlying causes of these productivity gains, we present mean values of the ratio of post to pre-MBO levels of output, total factor productivity, labor productivity, employment, capital/labor ratio, and the materials/labor ratio in Table 5. It appears that representative MBO plants generate considerably less output in the post-buyout period, yet downsize their workforce even more dramatically. This results in a significant increase in labor and total factor productivity. These findings, in conjunction with evidence on changes in capital/labor and materials/labor ratios, imply that the improvement in economic performance may be due to measures undertaken by new owners or managers to reduce the labor intensity of production, through the outsourcing of intermediate goods and materials.

VII. PRELIMNARY CONCLUSIONS AND SUGGESTIONS FOR ADDITIONAL RESEARCH Based on a comprehensive plant-level dataset, our preliminary results suggest that MBOs may be a useful mechanism for reducing agency costs and enhancing economic efficiency. More generally, the findings are consistent with recent theoretical and empirical evidence (see Jovanovic and Rousseau (2002a, 2002b) suggesting that takeovers result in the reallocation of a firm’s resources to more efficient uses and to better managers. An important extension of our analysis would be to examine whether the impact of MBOs on productivity varies for different types of MBOs. For instance, we might expect that private firms have lower agency costs than publicly-traded firms. That is because privately-held companies are usually owned and managed by a small, concentrated group of shareholders, typically consisting of a founder and his family. Thus, we might want to compare the TFP

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performance of public to private and private to private MBOs. It would also be interesting to assess whether there are differences in the effects of domestic and foreign MBOs on economic efficiency. Another type of MBOs that warrants further attention is the management buy-in (MBI). In theory, MBIs occur when incumbent managers do not possess the requisite skills to manage the firm (or unit of the firm) effectively, so an external management group is brought in to take the firm private. Thus, it is conceivable that the impact of an MBI on productivity may be greater than the impact of an MBO on economic performance. The occurrence of an MBI, rather than an MBO, may be indicative of shortcomings in the quality of management. The introduction of new, external equity-owning managers with more talent and experience could have a positive impact on the quest for higher TFP. Indeed, there is some evidence that managers involved in MBIs are more entrepreneurial than managers in MBOs (see Robbie and Wright (1996)).

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References Amess, Kevin (2002) “Management Buyouts and Firm-Level Productivity: Evidence from a Panel of U.K. Manufacturing Firms,” forthcoming in the Scottish Journal of Political Economy. Arellano, Manuel and Stephen R. Bond (1991) “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, 58, pp. 277-297. Arellano, Manuel and Stephen R. Bond (1998) “Dynamic Panel Data Estimation using DPD98 for GAUSS,” mimeo. Bartelsman, Eric J. and Mark Doms (2000) “Understanding Productivity: Lessons From Longitudinal Microdata,” Journal of Economic Literature, 38, pp. 569-594. Blundell, Richard and Stephen Bond (1999) “GMM Estimation with Persistent Panel Data: An Application to Production Functions,” IFS Working Paper Series No. W99/4. Caves, Richard E. (1998) “Industrial Organization and New Findings on the Turnover and Mobility of Firms, Journal of Economic Literature, 36, pp. 1947-82. Citron, David, Ken Robbie and Mike Wright (1997). “Loan Covenants and Relationship Banking in MBOs,” Accounting and Business Research, 27, pp.277-296. CMBOR (2002). “Trends in UK Buyouts,” Management Buy-outs – Quarterly Review from CMBOR, Nottingham: Centre for Management Buy-out Research, Spring, pp1-14. Griffith, Rachel (1999) “Using the ARD Establishment Level Data to Look at Foreign Ownership and Productivity in the United Kingdom,” Economic Journal, 109, pp. F416F442. Harris, Richard I.D. (2002) “Using the ARD Establishment Level Data to Look at Foreign Ownership and Productivity in the United Kingdom-A Comment,” forthcoming in the Scottish Journal of Political Economy. Harris, Richard I.D. and Steven Drinkwater (2000) “U.K. Plant and Machinery Capital Stocks and Plant closures,” Oxford Bulletin of Economic and Statistics, 62, pp. 239-261. Jensen, Michael, C. (1993) “The Modern Industrial Revolution: Exit and the Failure of Internal Control Systems,” Journal of Finance, 48, pp. 831-880. Jovanovic, Boyan and Peter Rousseau (2002a). "The Q-Theory of Mergers," American Economic Review, 92, pp. 198-204. Jovanovic, Boyan and Peter Rousseau (2002b). "Mergers as Reallocation," mimeo, Department of Economics, New York University. Kaplan, Steven (1989) “The Effects of Management Buyouts on Operating Performance and Value,” Journal of Financial Economics, 24, pp. 217-254. Kaplan, Steven (1991) “The Staying Power of Leveraged Buyouts,” Journal of Financial Economics, 29, pp. 287-313.

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Lichtenberg, Frank R. and Donald Siegel (1987)."Productivity and Changes in Ownership of Manufacturing Plants," Brookings Papers on Economic Activity, 3, pp. 643-673. Lichtenberg, Frank R. and Donald Siegel (1990a)."The Effect of Leveraged Buyouts on Productivity and Related Aspects of Firm Behavior," Journal of Financial Economics, 27, 165-194. Lichtenberg, Frank R. and Donald Siegel (1990b)."The Effect of Ownership Changes on the Employment and Wages of Central Office and Other Personnel," Journal of Law and Economics, 33, pp. 383-408. Lichtenberg, Frank R and Donald Siegel (1991). “The Impact of R&D Investment on Productivity-New Evidence Using Linked R&D-LRD Data," Economic Inquiry, 29, pp. 203-228. Maksimovic, Vojislav and Gordon Phillips (2001)."The Market for Corporate Assets: Who Engages in Mergers and Asset Sales and Are There Efficiency Gains?," Journal of Finance, 56, pp. 2019-2065. McGuckin, Robert H. and Sang V. Nguyen (1995)."On Productivity and Plant Ownership Change: Evidence From the Longitudinal Research Database," Journal of Law and Economics, 33, pp. 383-408. Oulton, Nicholas and Mary O’Mahony (1994) Productivity and Growth: A Study of British Industry, 1954-1986, The National Institute of Economic and Social Research Occasional Papers 47 (Cambridge University Press, Cambridge). Robbie, Ken and Mike Wright (1996) Management Buy-ins: Entrepreneurship, Active Monitoring and Corporate Restructuring. Manchester, U.K.: Manchester University Press. Shleifer, Andrei (2001). Inefficient Markets, New York: Oxford University Press. Siegel, Donald (1988). "Productivity Analysis Using Longitudinal Establishment Data," Ph.D. Thesis, Graduate School of Arts and Sciences, Columbia University. Smith, Abbie (1990). "Corporate Ownership and Performance: The Case of Management Buyouts," Journal of Financial Economics, Vol. 27, No. 1, pp. 143-164. Thompson, Steve and Mike Wright (1995) “Corporate Governance: The Role of Restructuring Transactions,” Economic Journal, Vol. 105, pp.690-703. Thompson, Steve, Mike Wright, and Ken Robbie (1992) “Management Equity Ownership, Debt, and Performance: Some Evidence from U.K. Management,” Scottish Journal of Political Economy, Vol. 39, pp. 413-430. Wright, Mike, Nick Wilson and Ken Robbie (1996). “The Longer Term Effects of Managementled Buy-outs,” Journal of Entrepreneurial and Small Business Finance, 5, pp.213-234.

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Table 1 Sources of U.K. Management Buy-outs (% by Number of Deals) Year 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Public 2.0 0.8 0.4 2.7 7.6 10.8 6.6 7.0 3.5 2.0 3.3 2.6 1.6 1.7 1.2 1.9 4.8 7.4 7.8 5.6

Full Firm MBOs Private 8.5 10.8 13.1 23.2 20.7 27.1 32.3 33.4 30.1 26.7 27.6 26.8 34.4 39.2 36.9 40.6 41.0 36.2 27.3 28.5

Divisional MBOs Domestic Foreign 50.6 8.1 54.6 9.2 50.2 9.6 49.1 9.9 47.6 11.4 37.2 7.8 39.6 7.2 44.9 5.8 40.7 6.6 39.5 8.3 40.7 6.1 35.2 11.8 35.1 10.3 32.7 9.5 34.1 6.7 30.9 7.1 31.5 7.0 36.0 4.5 41.8 7.7 36.6 10.2

Other 30.8 14.6 16.7 15.1 12.7 17.1 14.3 8.9 17.1 24.6 22.3 23.6 18.6 16.9 21.1 19.5 15.7 15.9 15.4 19.1

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Table 2 Sources of UK Management Buy-outs (% by Deal value) Year 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Public 46.9 0.4 0.2 6.6 18.1 17.1 21.3 58.1 12.2 3.1 1.9 1.0 6.2 1.4 0.9 4.8 18.5 27.7 41.6 25.4

Full Firm MBOs Private 6.3 8.5 9.5 10.1 10.8 8.8 13.1 6.8 16.5 16.5 17.7 12.0 22.1 29.7 17.2 22.0 18.3 13.7 4.2 10.4

Divisional MBOs Domestic Foreign 23.9 3.8 69.7 5.0 48.7 23.1 57.9 19.9 42.2 17.3 41.6 21.0 54.3 5.7 31.5 1.3 43.5 14.7 36.5 25.9 57.6 7.1 50.6 19.9 40.9 10.6 36.4 15.8 38.8 10.7 45.1 14.7 42.2 4.9 30.0 8.4 39.0 2.7 52.9 4.9

Other 19.1 16.4 19.5 5.5 11.6 11.5 5.6 2.3 13.1 18.0 15.7 16.5 20.2 16.7 32.4 13.4 16.1 20.2 12.5 6.4

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Table 3 Comparison of Mean Values of Output, Capital, Labor and Other Variables Used in the Plant-Level Production Function Analysis (For MBO and Non-MBO Manufacturing Plants in the United Kingdom, 1994-1998)

Variable

t-tests for Differences in Means

MBO Plants

Non-MBO Plants

3.3

4.2

6.29

0.7

1.1

5.95

Plant Employment

59

54

1.75

Percentage Foreign-Owned Percentage Located in “Assisted Areas”

6.9

11.9

13.44**

40.3

39.5

-1.15

Number of Plants

4877

30875

Real Gross Output (£m 1990 prices) Real Value of Plant & Machinery Capital Stock (£m 1980 prices)

**

**

Source: Authors calculations based on the linked ARD-CMBOR dataset; ** significant at 5% level.

22 Table 4 GMM Estimates of Plant-Level Augmented Production Functions for 35,752 U.K. Manufacturing Establishments for the Years 1988-98 (Eq. (3)) Dependent Variable: Logarithm of Real Gross Output Short-run

Coefficient on:

βˆ

Long-run

t-value

βˆ

t-value

Ln (LABOR)

0.2380

13.50

0.2574

13.74

Ln (MATERIALS)

0.6569

22.60

0.6629

25.89

Ln (CAPITAL )

0.1321

5.20

0.1336

5.79

AGE

-0.0631

-4.62

-0.0808

-4.69

AA (“Assisted Area”)

-0.0165

-4.09

-0.0212

-4.09

US OWNED PLANT

0.0689

10.00

0.0882

10.27

OTHER FOREIGN OWNER

0.0615

6.43

0.0788

6.50

PREMBO

-0.0157

-2.70

-0.0202

-2.70

POSTMBO

0.7049

4.94

0.9032

4.96

PREMBO × SIC24 (Non-Metallic Minerals)

0.4641

2.55

0.5946

2.55

POSTMBO× SIC24 (Non-Metallic Minerals)

-0.9969

-4.45

-1.2773

-4.45

PREMBO × SIC25 (Chemicals)

0.2874

7.70

0.3683

7.65

POSTMBO × SIC25 (Chemicals)

-0.3915

-3.64

-0.5017

-3.64

PREMBO × SIC33 (Office Machinery)

-0.1081

-2.14

-0.1385

-2.14

POSTMBO × SIC34 (Electrical Eng.)

-0.0598

-2.78

-0.0767

-2.78

POSTMBO × SIC42 (Drink/Tobacco.)

-0.2333

-5.09

-0.2990

-5.11

PREMBO × Ln (LABOR) × SIC24

-0.1410

-3.25

-0.1806

-3.25

POSTMBO × Ln (LABOR) × SIC24

-0.2721

-2.09

-0.3486

-2.09

PREMBO × Ln (MATERIALS) × SIC24

0.2664

3.22

0.3413

3.22

POSTMBO× Ln (MATERIALS) × SIC24

0.9069

5.28

1.1619

5.27

POSTMBO × Ln (CAPITAL ) × SIC24

-0.9202

-6.91

-1.1790

-6.88


0.0510

2.57

0.0653

2.57


-0.1957

-4.16

-0.2507

-4.14

PREMBO × Ln(CAPITAL ) × SIC25

0.1597

3.75

0.2046

3.73


0.0332

2.99

0.0653

2.99

POSTMBO × Ln (MATERIALS) × SIC31

-0.1317

-3.53

-0.1687

-3.53


0.0528

2.41

0.0677

2.41


0.0155

3.60

0.0199

3.59

23 PREMBO × Ln (LABOR) × SIC35

-0.0281

-2.73

-0.0360

-2.75


0.0500

2.93

0.0640

2.94


0.1183

3.74

0.1515

3.77

POSTMBO × Ln(MATERIALS) × SIC35

-0.1786

-3.72

-0.2289

-3.73

PREMBO × Ln (CAPITAL ) × SIC35

-0.0490

-2.49

-0.0628

-2.50


0.0944

2.51

0.1210

2.51


0.0674

2.53

0.0863

2.54


0.0282

3.65

0.0361

3.68


-0.0404

-4.30

-0.0518

-4.33


0.0569

2.68

0.0729

2.68


0.0208

3.96

0.0266

3.98


-0.0301

-2.46

-0.0386

-2.47


-0.0702

-5.18

-0.0900

-5.17


0.1651

3.82

0.2115

3.83


0.0573

2.23

0.0734

2.23


0.0361

2.12

0.0462

2.12


-0.1073

-2.63

-0.1374

-2.63

POSTMBO × Ln (CAPITAL ) × SIC46i

0.0479

2.38

0.0614

2.38

PREMBO × Ln(MATERIALS) × SIC47

0.0199

1.89

0.0255

1.90


0.0734

1.85

0.0941

1.85


-0.0375

-2.06

-0.0480

-2.06


-0.0700

-3.05

-0.0896

-3.03


0.0721

4.14

0.0924

4.12

t

0.0050

2.47

0.0065

2.47

Non-metallic minerals (SIC24)

0.0397

3.62

0.0508

3.63

Office machinery (SIC33)

0.2548

13.30

0.3264

13.42

Textiles (SIC43)

-0.0817

-9.14

-0.1047

-9.36

Paper, publishing (SIC47)

0.0505

6.69

0.0648

6.64

East Anglia

0.0202

2.70

0.0258

2.70

East Midlands

-0.0170

-2.83

-0.0218

-2.83

Yorks-Humberside

-0.0146

-3.26

-0.0187

-3.27

North West

-0.0124

-2.36

-0.0159

-2.36

North

-0.0120

-2.19

-0.0154

-2.19

Wales

-0.0130

-2.18

-0.0167

-2.18

24 Northern Ireland

-0.0266

-3.39

1.666

[0.65]

-24.180

[0.00]

1.900

[0.06]

Zero-slopes ~ χ (× 10 )

26.220

[0.00]

No. of plants

35,752

No. of observations

195,676

Diagnostic tests 2 Sargan ~ χ

-0.0340

-3.40

a

m1 ~ N(0,1) m2 ~ N(0,1) 2

5

Notes: The “short run” regressions include lagged values of OUTPUT, LABOR, MATERIALS, and CAPITAL as regressors, with coefficients and t-values (in parenthesis) of 0.220 (24.60), -0.037 (-10.04), -0.140 (-10.90), and 0.028 (-4.07), respectively. The model was estimated in PcGive 10 (Dynamic Panel Data Analysis); all t-values are based on robust standard errors; m1 and m2 are tests for first and second order serial correlation; the GMM estimator has instruments back to t − 12 for the model in first differences and ∆t−11 for the model in levels. a p-values in parenthesis

25

Table 5: Mean Values of Post-MBO to Pre-MBO Levels of Real Output, Labor Productivity, Employment, Capital/Labor Ratio, and Materials/ Labor Ratioa (All Figures are Weighted Percentages)

Industry (SIC)

Output

Total Factor Labor Productivit Productivit Plant Capital/Labor Materials/Labor y y employment Ratio Ratio

Total Manufacturing

50.5

108.6

112.0

39.0

84.9

137.9

Metal manufacturing (22)

49.1

103.3

124.6

47.8

40.0

113.4

Non-metallic minerals (24)

14.7

167.0

33.4

15.4

60.5

106.8

Chemicals (25)

36.4

93.1

74.4

32.7

68.1

113.2

Metal goods nec (31)

54.8

102.4

135.2

46.1

84.5

116.4

Mechanical Engineering (32)

88.5

101.6

148.7

68.9

75.2

143.8

Office machinery (33)

147.5

277.9

71.2

50.5

199.0

107.8

Electrical & electronics (34)

53.2

100.2

144.6

42.1

66.3

134.3

Motor vehicles (35)

57.0

100.4

101.1

52.2

135.1

120.4

Other transport equipment (36)

29.9

99.2

117.6

26.8

122.4

132.0

Instrumental engineering (37)

61.3

117.1

51.8

207.8

111.9

Food (41)

76.8

103.5

122.7

69.2

68.9

104.3

Drink & tobacco (42)

48.5

96.9

80.7

48.9

71.9

107.8

112.5

99.9

127.3

80.6

95.8

150.2

Footwear & Clothing (45)

89.1

100.0

112.4

70.1

73.6

128.7

Timber & Wood (46)

60.8

101.8

137.3

52.0

87.8

111.1

Paper & publishing (47)

59.7

107.9

147.4

46.4

76.1

139.3

Rubber & plastics (48)

69.0

102.5

149.3

60.6

84.5

126.5

Other manufacturing

82.6

100.6

107.2

62.9

230.3

122.6

Textiles (43)

100.9

26

(49) a

Note that only plants that experienced an MBO are included in this analysis (4877 plants).

27

Figure 1 U.K. MBO Trends (1980 -2001*) Sour ce: CMBOR/Barclays Private Equity/Deloitte & Touche * Year 2001 figur es arefor first 9 months only

800

30000

700

25000

400

15000

300

10000

200 5000

Total Number

Total Value (£m)

2000

1998

1996

1994

1992

1990

1988

1986

1984

0

1982

100

0

£ million

20000

500

1980

Number

600

28

Figure 2 MBOs as a % of U.K. Takeover Activity (1987 Source: CMBOR/Barclays Private Equity/Deloitte & Touche, * Year

-2001*)

2001 figur es ar e forfirst 9months only

70 60 50 40 30 20 10 0 1987

1988

1989 1990

1991

1992 1993

1994 1995

Nu mb er(%)

1996

Value (%)

1997 1998

1999

2000 2001*