The relationship between ownership structure and performance in listed Australian companies.

Abstract:

This paper examines the relationship between ownership structure and corporate performance in Australian listed companies. The study applies the models advanced by Demsetz and Villalonga (2001), examining the relationship between ownership and performance when ownership is modelled as a multi-dimensional endogenously determined variable. OLS results suggest that ownership is significant in explaining performance. However, when endogeneity is taken into account, ownership is not statistically dependent on the performance measure. Finally, previous research by authors including Morck, Schleifer and Vishny (1988) suggests' that the relationship between ownership and performance is nonlinear. We fit a generalised nonlinear model that nests models advanced previously. Results provide limited evidence of a nonlinear relationship between managerial share ownership and firm performance.

Keywords:

OWNERSHIP STRUCTURE; PERFORMANCE; ENDOGENEITY OF OWNERSHIP STRUCTURE; NONLINEARITY.

**********

1. Introduction

The relationship between ownership structure and corporate performance is one that has received considerable attention in the finance literature. However, a notable feature of this body of literature is its failure to reach a consensus regarding the nature of the relationship. Demsetz and Villalonga (2001) posit that the conflicting results may stem from differences with respect to the measurement of variables, sample period, estimating technique and whether or not the research explicitly accounts for the endogeneity of a firm's ownership structure, that is documented by Demsetz (1983) and Demsetz and Lehn (1985), among others. Demsetz and Villalonga (2001, p. 211) stress that not only should the endogeneity of ownership structure be accounted for, ownership should be modelled 'simultaneously, as an amalgam of shareholdings owned by persons with difference interests. In particular, the fractions of shares owned by outside shareholders and by management should be measured separately'. The failure of previous research to 'examine two dimensions of this structure likely to represent conflicting interests' (1) provides a motivation for Demsetz and Villalonga (2001) to re-examine the relationship between ownership structure and corporate performance.

The current paper revisits the work of authors including Morck, Schleifer and Vishny (1988) and Demsetz and Villalonga (2001), applying similar models to Australian listed companies. The study seeks to add to the limited evidence regarding this relationship in the Australian context, where extant studies have failed to account for the endogeneity of ownership structure, and consider whether results are consistent with those found for American companies. The remainder of this paper is organised as follows. Section 2 reviews the previous studies that examine the relationship between ownership structure and corporate performance, including research that has been conducted using Australian data. Section 3 provides formal model specifications to be fit using the data outlined in section 4. Section 5 describes results as well and discusses their robustness to the use of alternate performance measures. These results are summarised in section 6.

2. Literature Review

Berle and Means (1932) are among the first to consider the relationship between a firm's ownership structure and its performance. They assert that as the diffuseness of ownership increases, shareholders become powerless to control professional managers. Further, they argue that, given the interests of management and shareholders are not generally aligned, corporate resources are not used efficiently in maximising corporate profit. Therefore, Berle and Means (1932) suggest that the relationship between ownership concentration and performance should be a negative one. However, Demsetz (1983, p. 386) argues 'it is unreasonable to suppose that diffuse ownership has destroyed profit maximisation as a guide to resource allocation'. Instead, he asserts that a firm's ownership structure is 'an endogenous outcome of a maximising process in which more is at stake than just accommodating to the shirking problem': (2)

   'When scale requirements are large, especially when the survival of
   the firm requires a rapid attainment of large scale, then there will
   be economic pressure to satisfy the consequent need for sizable
   equity capital by turning to a diffuse ownership structure.... The
   greater monitoring cost that might arise from such an ownership
   structure may be more than offset by the reduction in
   risk-associated capital cost, so that maximisation of the value of
   the assets of the firm actually requires a diffuse ownership
   structure.... No single ownership structure is suitable for all
   situations if the value of the firm's assets is to be maximised.'

A summary of empirical studies examining the nature of the relationship between ownership and performance is presented in table 1. Despite the conceptual and empirical support for the endogeneity of ownership structure, many studies have failed to take this endogeneity into account when estimating the effect of ownership structure on performance (see Morck, Shleifer & Vishny 1988; McConnell & Servaes 1990; and, in the Australian context, Craswell, Taylor & Saywell 1997). The importance of accounting for the endogeneity of ownership is further emphasised by the work of Demsetz and Villalonga (2001). Modelling ownership as a multi-dimensional variable that separately reflects the fraction of shares owned by outsiders and management and performance, Demsetz and Villalonga (2001) find ordinary least squares testing suggests that firm performance is always dependent on at least one measure of ownership structure. However, when testing is performed using a 2-stage least squares approach, which accounts for the possible endogeneity of ownership structure, neither measure of ownership structure is statistically significant in explaining variation in performance. Demsetz and Villalonga (2001, p. 227) argue these results 'are consistent with the view that ownership structure is chosen so as to maximise firm performance, and that the greater diffuseness in ownership, although it makes the agency problem more severe, conveys compensating advantages on firms that choose to rely on a diffuse ownership structure'.

Demsetz and Villalonga (2001) note other fundamental differences in previous studies, including differences in variable measurement, as well as the failure of many researchers to acknowledge that ownership is an amalgam of shareholdings owned by people with potentially divergent interests. These variations, which are clearly evidenced in table 1, may provide an explanation for the failure of the extant literature to reach a consensus regarding the nature of the relationship between ownership and corporate performance.

3. Model Specification

The review of extant literature in section 2 shows the considerable variation in ownership and performance measures employed in studies examining the relationship between the two variables. This study will adopt the variable definitions used by Demsetz and Villalonga (2001). More specifically, two ownership variables are considered in the paper: the shareholdings of the firm's 5 largest shareholders ('TOP5'); and, the shareholdings of the firm's top management and board of directors ('MSO'). Both ownership variables are defined in greater detail as part of the formal model specification below.

As noted by Demsetz and Villalonga (2001), it is important to differentiate between ownership by top shareholders and the board as these two measurements provide an indication of the relative power held by two parties with potentially diverging interests. Moreover, the percentage of shares owned by a company's top 5 shareholders indicates the ability of outside shareholders to control the actions of management. Conversely, the level of board ownership indicates the ability of directors to ignore the wishes of other shareholders. Therefore, these two measures capture the interests of two groups with arguably the most divergent interests.

In addition to the ownership variables described above, two measures of performance are collected: Tobin's Q ('Q'); and, average accounting profit rate ('PRATE'). Both performance measures are more comprehensively defined as part of the formal model specifications below. While Tobin's Q is the most common measure that has been used to date in modelling the relationship between ownership structure and corporate performance, it is important to test the robustness of reported results to the use of an alternate performance measure. It is for this reason that modelling is also performed using accounting profit rate as the performance measure.

Finally, when modelling the relationship between ownership structure and corporate performance, it is also necessary to control for firm-specific characteristics. The inclusion of such variables allows for the possibility that a number of factors jointly affect ownership structure or corporate performance and therefore induce spurious correlation between them. In considering which control variables to include in the models fitted, previous research in the area was consulted. Table 2 shows control variables used by researchers that have examined the relationship between ownership structure and corporate performance previously.

Control variables used in the current study are those employed by Demsetz and Villalonga (2001). Firstly, it is necessary to account for the intangible assets that we could expect to impact on Tobin's Q. We may expect the existence of intangible assets to distort Tobin's Q given the total book value of assets, which represents the denominator of Tobin's Q, may not include the value of all intangibles. Observable measures of these intangible assets include a firm's research and development and property plant and equipment expenditures. Previous studies have also included advertising expenditure as an additional measure of intangible assets that may distort Tobin's Q (e.g. Morck, Sehleifer & Vishny 1988; Demsetz & Villalonga 2001, among others). However, given Australian accounting standards, which do not require the disclosure of this expenditure, a similar variable was not included in the current study.

Further, modelling should control for firm leverage, as pecking order theory predicts a negative correlation between a firm's debt levels and corporate performance. (3) Also, firm leverage provides a measure of monitoring provided by credit providers, which may reduce the need for additional monitoring provided by concentrated ownership.

The inclusion of a measure of firm-specific risk takes account of the fact that there are different levels of risk associated with investing in different companies. It is possible that ownership concentration may vary in line with the level of firmspecific risk. Also, as Demsetz and Villalonga (2001, p. 222) note higher levels of market and firm-specific risk 'indicate better prospects to profit form the use of inside information, and, therefore, a stronger causation effect that runs from variations in expected firm performance to variations in management shareholdings'.

In modelling the relationship between ownership structure and corporate performance, it is also necessary to control for firm size to account for the possibility that performance and ownership are related through the size of the firm. More specifically, it is quite possible that either top shareholders or insiders are able to obtain larger fractions of shares in smaller firms, but firm size and Tobin's Q could be expected to be negatively related to one another.

The inclusion of utility and finance industry dummy variables is important, as they control for the possibility of spurious correlation between ownership structure and corporate performance that stems from industry effects (e.g. Demsetz & Lehn 1985). This correlation may result from the relative advantage to investors of obtaining large stakes in a firm caused by regulations within a given industry that may serve to limit how shareholders can use the firm's assets. Finally, the inclusion of a media dummy variable reflects the fact it is an industry that displays what Demsetz and Lehn (1985) refer to as amenity potential, or investor utility additional to that generated by profitability. They argue that given the existence of this amenity potential, they would expect ownership within media firms to be more concentrated, all other factors constant.

The econometric model developed by Demsetz and Villalonga (2001) comprises two equations. These equations are also tested in the current paper, and are formally presented below:

(1) Q = [[beta].sub.0] + [[beta].sub.1]MSO + [[beta].sub.2]TOP5 + [[beta].sub.3]RDSALE + [[beta].sub.4]FIXSALE + [[beta].sub.5]DEBTASSET + [[beta].sub.6]UTIL + [[beta].sub.7]MED+ [[beta].sub.0]FIX

where: Q = the average of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs are calculated as [(year-end book value of debt + year-end market value of equity) / year-end book value of assets];

MSO = log (RAWMSO / (100 - RAWMSO)), where RAWMSO is the average year-end percentage of ordinary shares owned by top management and the board calculated over 1999 and 2000;

TOP5 = log (RAWTOP5 / (100 - RAWTOP5)), where RAWTOP5 is the percentage of ordinary shares owned by the top 5 shareholders in 2000;

RDSALE = the average ratio of annual research and development expenditure to annual sales, calculated over 2 years (1999 and 2000);

FIXSALE = the average ratio of the change in gross fixed assets to annual sales, calculated over 2 years (1999 and 2000);

DEBTASSET = the average ratio of year-end debt to the year-end book value of assets, calculated over 2 years (1999 and 2000);

UTIL = a utility company indicator variable (which is equal to 1 if the firm operates in the utility industry, and 0 otherwise) (4);

MED = a media company indicator variable (which is equal to 1 if the firm operates in the media industry, and 0 otherwise) (4); and

FIN = a finance company indicator variable (which is equal to 1 if the firm operates in the finance industry, and 0 otherwise). (4)

(2) MSO = [[beta].sub.0] + [[beta].sub.1]DEBTASSET + [[beta].sub.2]UTIL + [[beta].sub.3]MED + [[beta].sub.4]FIN + [[beta].sub.5]Q + [[beta].sub.6]MKTRISK + [[beta].sub.7]FIRMRISK + [[beta].sub.8]ASSET

where: MSO = log (RA WMSO / (100 - RAWMSO)), where RAWMSO is the average year-end percentage of ordinary shares owned by top management and the board calculated over 1999 and 2000;

DEBTASSET = the average ratio of year-end debt to the year-end book value of assets, calculated over 2 years (1999 and 2000);

UTIL = a utility company indicator variable (which is equal to 1 if the firm operates in the utility industry, and 0 otherwise) (5);

MED = a media company indicator variable (which is equal to 1 if the firm operates in the media industry, and 0 otherwise) (5);

FIN = a finance company indicator variable (which is equal to 1 if the firm operates in the finance industry, and 0 otherwise) (5);

Q = the average of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs are calculated as [(year-end book value of debt + year-end market value of equity) / year-end book value of assets];

MKTRISK = the beta coefficient obtained from a regression of monthly stock returns on monthly market returns using stock price data from July 1998 to June 2002 inclusive (6);

FIRMRISK = the standard error obtained from the regression used to estimate MKTRISK; and

ASSET = the average year-end book value of assets calculated across 1999 and 2000.

Consistent with Demsetz and Villalonga (2001), the maximum likelihood estimates of [[beta].sub.1], [[beta].sub.2]...., [[beta].sub.8] for both equations (1) and (2) are obtained through both ordinary least squares and, in order to account for endogeneity, 2-stage least squares approaches. Results are reported in sections 5.1 and 5.2. These sections also consider the robustness of these results to the use of average accounting profit rate ('PRATE'), which is calculated as the average across 1999 and 2000 of annual net income to the year-end book value of equity, instead of average Tobin's Q.

Examination of table 1 shows that previous research has also found some support for a non-linear relationship between ownership structure and corporate performance. Given this, a general non-linear model specification that nests the linear, quadratic and piecewise models fitted in previous research is considered. The models employed previously are considered in detail in tables 1 and 2. The added benefit of the model fit in the current paper is that it affords continuity and fits a very general non-linear specification. The model is formally defined as follows, with results reported in section 5.3. While Bayesian variable selection could be employed to select the knot points included in the regression (7), the inclusion of knots used in earlier research allows us to nest their specifications in our model. The model is also estimated using accounting rate of return as an alternate performance measure:

(3) Q = [[beta].sub.0] + [[beta].sub.1]RDSALE + [[beta].sub.2]FIXSALE + [[beta].sub.3]DEBTASSET + [[beta].sub.4]UTIL + [[beta].sub.5]MED + [[beta].sub.6]FIN + [[beta].sub.7]RAWMSO + [[beta].sub.8][RAWMSO.sup.2] + [[beta].sub.9][RAWMSO.sup.3] + [[beta].sub.10][KNOT5.sup.3] + [[beta].sub.11][KNOT25.sup.3]

where: Q = the average of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs are calculated as [(year-end book value of debt + year-end market value of equity) / year-end book value of assets];

RDSALE = the average ratio of annual research and development expenditure to annual sales, calculated over 2 years (1999 and 2000);

FIXSALE = the average ratio of the change in gross fixed assets to annual sales, calculated over 2 years (1999 and 2000);

DEBTASSET = the average ratio of year-end debt to the year-end book value of assets, calculated over 2 years (1999 and 2000);

UTIL = a utility company indicator variable (which is equal to 1 if the firm operates in the utility industry, and 0 otherwise) (8);

MED = a media company indicator variable (which is equal to 1 if the firm operates in the media industry, and 0 otherwise) (8); and,

FIN = a finance company indicator variable (which is equal to 1 if the firm operates in the finance industry, and 0 otherwise) (8);

RAWMSO = the average year-end percentage of ordinary shares owned by top management and the board calculated over 1999 and 2000;

[RAWMSO.sup.2] = RAWMSO squared;

[RAWMSO.sup.3] = RAWMSO cubed;

[KNOT5.sup.3] = max [(RAWMSO - [k.sub.1], 0).sup.3], where [k.sub.1] represents a knot point at 5%. The 5% knot point is taken directly from the work of Morck, Schleifer and Vishny (1988), among others; and,

[KNOT25.sup.3] = max [(RAWMSO - [k.sub.2], 0).sup.3], where [k.sub.2] represents a knot point at 25%. The 25% knot point is also based on the work of authors including Morck, Schleifer and Vishny (1988).

4. Data

The sample utilised in this study comprises data for 114 public companies listed on the Australian Stock Exchange. Data was collected for these companies in respect of the period 1999 to 2000 inclusive, with the sample including every company for which all data was available. Accounting information was collected from Compustat, ownership data was obtained from Connect 4, share price information was downloaded from Datastream, and market risk measures as well as information used to calculated measures of firm-specific risk were obtained from the Risk Measurement Service within the Centre for Research in Finance at the Australian Graduate School of Management. Table 3 provides an overview of characteristics of companies forming part of the final sample, and shows that the sample includes companies of different size and industry membership.

Summary statistics for variables collected in respect of each company forming part of the final sample are presented in table 4, below. Table 5 provides a summary of correlations between variables collected for each company. Examination of this table reveals nothing of concern.

5. Results

5.1 OLS Results

Table 6 presents the results for the Demsetz and Villalonga (2001) replication employing an ordinary least squares methodology. Most notably, results show that performance, as measured by Q, is not statistically dependent on either measure of ownership. Further, ownership is not statistically dependent on Q. However, when accounting rate of return is employed as the performance measure, performance is dependent on the level of managerial share ownership and vice versa. The positive coefficient on the managerial share ownership coefficient in the regression where performance is measured by accounting rate of return is consistent with the idea that, as managerial share ownership increases, management has greater incentive to maximise firm performance. These results are somewhat inconsistent with Demsetz and Villalonga (2001) who report that both measures of performance are always statistically dependent on at least one of the two ownership measures and ownership, but the reverse is not true.

Examination of table 6 also reveals that, irrespective of the performance measure employed, the ratio of debt to assets is a negative and significant predictor of firm performance. This is consistent with the idea that, as debt rises, so too do the costs associated with servicing it and, as a result, firm performance declines. Further, when performance is measured by Q, the ratio of research and development expenditure to sales is a statistically significant positive predictor of performance. Conversely, Q is negatively statistically dependent the ratio of the change in fixed gross assets to sales.

Results for equation (2) also suggest that firm industry is important in explaining variation in corporate ownership structures, with both MED and FIN coefficients statistically significant. The positive sign on both these industry coefficients suggests that, all else constant, ownership is more concentrated in media and financial firms relative to companies operating in other industries. These results are consistent with the idea of amenity potential advanced by Demsetz and Lehn (1985).

Finally, irrespective of the performance measure employed, results suggest that ownership is statistically dependent on firm size. The negative firm size coefficient is consistent with the idea that, as firms become larger, ownership concentration decreases, as shareholders have to invest greater amounts to obtain a given level of shareholdings.

5.2 Two-Stage Least Squares Results

The results for the Demsetz and Villalonga (2001) replication using 2-stage least squares are reported in table 7. Results show that, once the endogeneity of ownership structure has been accounted for, ownership concentration is not significant in explaining firm performance. This outcome is consistent with the findings of Demsetz and Villalonga (2001), and provides evidence of the endogeneity of ownership structure within the Australian context. With the exception of these changes, results of the 2-stage least square testing are similar to those obtained for ordinary least squares.

5.3 Results of General Non-Linear Model Fit

Results for the general non-linear model specification (equation (3)) are reported in Table 8. Examination of the table shows that the results are similar to those for the ordinary least squares fit of equation (1). The only notable difference is that the newly included variable accounting for a knot point at managerial ownership of 25% is negative and statistically significant. This result is inconsistent with the findings of Morck, Schleifer and Vishny (1988) who report a statistically significant positive relationship for managerial share ownership in excess of 25% and firm performance. However, similar to Morck, Schleifer and Vishny (1988), our results are not robust to the use of accounting profit rate as an alternative measure of firm performance. When PRATE is employed as the performance proxy, neither knot point variable is statistically significant. Instead, the ratio of debt to assets is a statistically significant negative predictor of performance. This is consistent with the idea that the increase in interests costs associated with higher levels of debt leads to reduced corporate profitability.

6. Conclusion

The current study was primarily motivated by a lack of evidence regarding the relationship between ownership structure and corporate performance in Australian companies. More specifically, previous research has not considered the relationship between these two variables when ownership is modelled as multi-dimensional and endogenously determined. Results suggest that the endogeneity problem documented in the American context is also present in Australia, with ordinary-least-squares testing finding that performance is statistically dependent on managerial ownership. However, when endogeneity is taken into account, performance exhibits no statistical dependence on either ownership measure.

Finally, previous research has provided evidence that the relationship between managerial share ownership and firm performance may be a non-linear one. To test this result, we fit a generalised nonlinear model that nests models advanced in earlier research. Results provide limited evidence of a nonlinear relationship between managerial share ownership and firm performance.

Table 1
Summary of Previous Studies Examining the Relationship Between
Ownership Structure and Corporate Performance

Authors                      Ownership Measure/s

Demsetz and         1. % of shares held by top  5 shareholders
Lehn (1985)         2. % of shares held by top 20 shareholders
                    3. Herfindahl measure of ownership concentration
                    4. % of shares controlled by top 5 families and
                       individuals
                    5. % of shares controlled by institutional
                       investors
Morck, Shleifer     % of shares held by company directors
and Vishay (1988)

McConnell and       1. % of shares held by insiders
Servaes (1990)      2. % of shares held by blockholders
                    3. % of shares held by institutional investors
Hermalin and        % of shares held by the current CEO and past CEOs
Weisbach (1991)     still on the board
Loderer and         % of shares held by officers and directors
Martin (1997)

Craswell, Taylor    1. % of shares held by company directors
and Saywell         2. % of shares owned by institutional investors
(1997)

Cho (1998)          % of shares held by company directors

Himmelberg,         % of shares held by insiders managers and directors
Hubbard and Palia
(1999)
Holderness,         % of shares held by officers and company directors
Kroszner and
Sheehan (1999)
Demsetz and         % of shares held by top management, the CEO and
Villalonga (2001)   company directors

Authors             Performance               Methodology
                    Measure/s

Demsetz and         Post-Tax Accounting   Ordinary Least Squares
Lehn (1985)         Profit/Book Value     Regression
                    of Equity
Morck, Shleifer     1. Tobin's Q          Piecewise Linear Regression
and Vishay (1988)   2. Accounting
                       Profit Rate
McConnell and       Tobin's Q             Ordinary Least Squares
                                          Regression
Hermalin and        Tobin's Q             Piecewise Linear
Weisbach (1991)                           Regression
Loderer and         Tobin's Q             Simultaneous Equations
Martin (1997)

Craswell, Taylor    Proxy Tobin's Q       1. Linear Regression
and Saywell         (market value of      2. Curvilinear Regression
(1997)              equity/book value     3. Piecewise Regression
                    of net assets)
Cho (1998)          Tobin's Q             1. Piecewise Linear
                                             Regression
                                          2. System of 3 Equations
Himmelberg,         Tobin's Q             1. Quadratic Piecewise
Hubbard and Palia                            Model
(1999)                                    2. Piecewise Linear Model
Holderness,         Tobin's Q             Piecewise Linear Regression
Kroszner and
Sheehan (1999)
Demsetz and         1. Tobin's Q          1. Ordinary Least Squares
Villalonga (2001)   2. Accounting            Regression
                       Profit Rate        2. 2-Stage Least Squares

Authors              Ownership    Results
                    Endogenous?

Demsetz and            Yes        No Significant Relationship
Lehn (1985)
Morck, Shleifer        No         Significant Non-Monotonic
and Vishay (1988)                 Relationship
McConnell and          No         Significant Curvilinear Relationship
Serves (1990)
Hermalin and           Yes        Significant non-monotonic
Weisbach (1991)                   relationship
Loderer and            Yes        Ownership doesn't predict
Martin (1997)                     performance, but performance is a
                                  negative predictor of ownership.
Craswell, Taylor       No         Weak curvilinear relationship.
and Saywell
(1997)
Cho (1998)             Yes        Firm performance affects ownership
                                  structure, but not vice versa.
Himmelberg,            Yes        Quadratic form of ownership effect
Hubbard and Palia                 on performance
(1999)
Holderness,            Yes        Significant non-monotonic
Kroszner and                      relationship
Sheehan (1999)
Demsetz and            Yes        No significant relationship
Villalonga (2001)

Table 2
Control Variables Used by Previous Studies in Modelling the
Relationship Between Ownership Structure and Corporate Performance *

Authors         Firm Size     Financial      Liquidity    Total Risk of
                (Measure)     Leverage                      the Firm
                              (Measure)                     (Measure)

Demsetz and     Yes           No             No           Yes
Lehn (1985)     (Av. MV                                   ([sigma] of
                common                                    stock returns
                equity/BV                                 and [sigma]
                assets) (h)                               of. Ace ROR)
Morck,          Yes           Yes            No           No
Shleifer and    (RC assets)   (MV long-
Vishny (1988)                 term debt/RC
                              assets)
McConnell       Yes           Yes            No           No
and Servaes     (RC assets)   (Debt/RC
(1990)                        assets)
Hermalin and    Yes           No             No           No
Weisbach        (ln(RC
(1991)          assets))

Loderer and     Yes           No             No           Yes
Martin          (ln(sales))                               ([sigma] and
(1997) (a)                                                [[sigma].
                                                          sup.2] of
                                                          stock
                                                          returns)
Craswell,       Yes           Yes            No           No
Taylor and      (ln(BV        (BV debt/BV
Saywell         assets))      assets)
(1997)
Cho (1998)      Yes           Yes            Yes          No
                (log(RC of    (MV long-      (Cashflows
                assets))      term debt/RC   (b)/RC of
                              assets)        assets)
Himmelberg,     Yes           No             No           No
Hubbard and     (ln(sales)
Palia           and
(1999) (d)      (ln(sales))
                (2))
Holderness,     Yes           Yes            No           No
Kroszner and    (Total        (Debt/
Sheehan         assets)       assets)
(1999)
Demsetz and     Yes           Yes            No           No
Villalonga      (Av. BV       (Av. debt/BV
(2001)          assets) (h)   assets) (14)

Authors           Market     Firm-Specific   Advertising       R&D
                   Risk          Risk        Expenditure    Expenditure
                (Measure)     (Measure)       (Measure)      (Measure)

Demsetz and     No           Yes             Yes             Yes
Lehn (1985)                  (std. error     (Sales/         (Sales/
                             from market     advertising)    R&D)
                             market model)
Morck,          No           No              Yes             Yes
Shleifer and                                 (Advertising/   (R&D/RC
Vishny (1988)                                RC assets)      assets)
McConnell       No           No              Yes             Yes
and Servaes                                  (Advertising/   (R&D/RC
(1990)                                       RC assets)      assets)
Hermalin and    No           No              Yes             Yes
Weisbach                                     (Advertising    (R&D
(1991)                                       weighted by     weighted
                                             RC assets)      by RC
                                                             assets)
Loderer and     No           No              No              No
Martin
(1997) (a)
Craswell,       No           No              No              Yes
Taylor and                                                   (R&D/BV
Saywell                                                      assets)
(1997)
Cho (1998)      No           Yes             No              Yes
                             ([sigma] of                     (R&D/RC
                             changes in                      assets)
                             (profit (f)/
                             RC assets)
Himmelberg,     No           Yes             Yes (f)         Yes (f)
Hubbard and                  ([sigma] of     (Advertising/   (R&D/PP&E)
Palia                        idiosyncratic   PP&E)
(1999) (d)                   stock price
                             risk) (e)
Holderness,     No           Yes             No              No
Kroszner and                 (std. error
Sheehan                      from market
(1999)                       model).
Demsetz and     Yes          Yes             Yes             Yes
Villalonga      ([beta]      (std. error     (Av.            (Av.
(2001)          from         of market       (advertising/   (R&D/
                regression   risk measure)   sales)) (h)     sales))
                of stock                                     (h)
                returns on
                market
                returns)

Authors           Capital       Industry
                Expenditure    Membership
                 (Measure)     (Measure)

Demsetz and     Yes            Yes
Lehn (1985)     (Sales/        (Dummies for
                capex)         utility and
                               media firms)
Morck,          No             Yes
Shleifer and                   (Dummies
Vishny (1988)                  based on SIC
                               codes)
McConnell       No             No
and Servaes
(1990)
Hermalin and    No             No
Weisbach
(1991)
Loderer and     No             No
Martin
(1997) (a)
Craswell,       No             Yes
Taylor and                     (Dummies
Saywell                        based on ASX
(1997)                         classes)
Cho (1998)      Yes            Yes
                (Capex/RC      (Dummies
                assets)        based on SIC
                               codes)
Himmelberg,     Yes (g)        No
Hubbard and     (Capex/
Palia           PP&E
(1999) (d)      expenditure)
Holderness,     No             Yes
Kroszner and                   (Dummies
Sheehan                        based on SIC
(1999)                         codes)
Demsetz and     Yes            Yes
Villalonga      (Av. (PP&E     (Dummies for
(2001)          expenditure/   utilities, media
                sales)) (h)    and financial
                               firms)

Note: * The table provides a summary of control variables used in
various tests during each paper. In many instances, only a fraction of
all variables listed for a paper were fitted in any given model.
Nonetheless, the table provides a useful summary of variables
considered in modelling the relationship between ownership structure
and corporate performance.

NB: RC = Replacement cost; BV = Book value; and, MV = Market value; SIC
Codes = Standard Industrial Classification Codes; Capex = Capital
expenditure;

(a.) Loderer and Martin's (1997) study uses acquisition data to examine
the relationship between ownership structure and corporate performance.
Therefore, in addition to the variables listed, the authors also
include a dummy variable indicating whether acquisitions are financed
by stock.

(b.) Cash flows in the Cho (1998) study are defined as after-tax
income + depreciation + amortisation.

(c.) Profit is defined as profit before extraordinary items.

(d.) Himmelberg et al. (1999) also include the (operating income /
sales) in modelling as a proxy for a firm's market power as well as a
measure of cash flows stemming from operations.

(e.) This value was calculated as the standard error of the residuals
from a CAPM model estimated using daily data.

(f.) Himmelberg et al. (1999) include dummy variables to indicate
whether firms separately disclosed their R&D and advertising
expenditures.

(g.) Himmelberg et al. (1999) also include (PP&E/Sales) and (PP&E/
Sales) (2) to measure reduction in agency costs resulting from the fact
these assets are easily monitored.

(h.) Averages are calculated using 5 years of data.

Table 3
Characteristics of Companies Forming Part of the Final Sample

                  Panel A: Firm Size (a)

<$50 million   $50 million-   $100 million-   $500 million-
               $100 million   $500 million     $1 billion
     11            19             37               20

 Panel A: Firm Size (a)

$1 billion-   >$5 billion
 $5 billion
    22            5

                  Panel B: Industry Membership (b)

Energy   Materials (c)   Industrials (d)       Consumer
                                           Discretionary (e)
  6           22               23                 25

                  Panel B: Industry Membership (b)

 Consumer     Health     Financials (h)    Information     Telecommm
Staple (f)   Cares (g)                    Technology (i)   unications
   13            9             4                6              3

Panel B: Industry Membership (b)

          Utilities
             3

Note: (a) Firm size is based on average market capitalisation at firms'
reporting dates in 1999 and 2000.

(b) Sector classification is based on the Global Industry Classification
Standard ('GIGS').

(c) Materials includes firms in the following classes: chemicals;
construction materials; diversified metals and mining; gold; precious
metals and minerals; steel and aluminium; and, paper, forest products
and packaging.

(d) Industrials includes firms in the following classes: building
products; construction and engineering; machinery; conglomerate and
other capital goods; commercial services and supplies; and,
transportation.

(e) Consumer discretionary includes firms in the following classes:
automobile and components; consumer durables and apparel; hotels,
restaurants and leisure; media; and, retailing.

(f) Consumer staple includes firms in the following classes: food and
drug retailing; beverages; and, food, other products and tobacco.

(g) Health care includes firms in the following classes: health care
equipment and supplies; health care providers and services; and,
pharmaceuticals and biotechnology.

(h) Financials includes firms in the following classes: banks;
diversified Financials; insurance; real estate investment trusts; and,
real estate management and development.

(i) Information technology includes firms in the following classes:
internet software and services; IT consulting and services; software;
and, technology hardware and equipment.

Table 4
Summary Statistics for Variables Used in Modelling the
Relationship Between Ownership Structure and Corporate
Performance

The notation used in the table below is defined as follows: Q is the
average of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs
are calculated as [(book value of debt + market value of equity)/book
value of assets]; PRATE is the average of annual profit rates for 1999
and 2000. Annual profit rates are calculated as (net income / book
value of equity); RAWMSO is the average year-end percentage of ordinary
shares owned by top management and the board calculated over 1999 and
2000; MSO is the log of (RAWMSO / (100 - RAWMSO)); RAWTOP5 is the
percentage of ordinary shares owned by the top 5 shareholders in 2000;
TOPS is the log of (RAWTOP5 / (100 - RAWTOP5)); RDSALE is the average
ratio of annual research and development expenditure to annual sales,
and is calculated as the average of the 1999 and 2000 ratios; FIXSALE
is the average ratio of the change in gross fixed assets to annual
sales, and is calculated as the average of the 1999 and 2000 ratios;
DEBTASSET is the average ratio of debt to the book value of assets, and
is calculated as the average of the 1999 and 2000 ratios; MKTRISK is
the [beta] coefficient obtained from a regression of monthly stock
returns on monthly market returns; FIRMRISK is the standard error of
the [beta] estimate obtained as the measure of MKTRISK; and, ASSET is
the average book values of assets across 1999 and 2000.

Variable          Mean    Standard       25th       Median     75th
                          Deviation   Percentile             Percentile

Q                1.8051      2.6429      0.7630     1.1132      1.9461
PRATE            0.1216      0.7117      0.0470     0.1557      0.2895
RAWMSO          11.1482     19.8167      0.1100     0.8424     13.8805
MSO             -1.9087      1.3293     -2.9581    -2.0708     -0.7937
RAWTOP5         48.8753     19.6627     35.3750    46.1000     63.0600
TOP5            -0.0242      0.4824     -0.2753    -0.0731      0.2064
RDSALE           0.0486      0.2711      0.0000     0.0000      0.0012
FIXSALE         -0.3373      5.3107      0.0091     0.0490      0.1758
DEBTASSET        0.2483      0.2086      0.1069     0.2451      0.3381
MKTRISK          1.2378      1.0728      0.5625     0.9050      1.7025
FIRMRISK         0.2729      0.1523      0.1688     0.2210      0.3289
ASSET         1194.3010   2008.2970    118.7901   351.1343   1008.2230
($ million)

Table 5
Correlation Matrix

The notation used in the table below is defined as follows: Q is the
average of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs
are calculated as [(book value of debt + market value of equity)/book
value of assets]; PRATE is the average of annual profit rates for 1999
and 2000. Annual profit rates are calculated as (net income / book
value of equity); RAWMSO is the average year-end percentage of ordinary
shares owned by top management and the board calculated over 1999 and
2000; MSO is the log of (RAWMSO / (100 - RAWMSO)); RAWTOP5 is the
percentage of ordinary shares owned by the top 5 shareholders in 2000;
TOP5 is the log of (RAWTOPS / (100 - RAWTOP5)); RDSALE is the average
ratio of annual research and development expenditure to annual sales,
and is calculated as the of the 1999 and 2000 ratios; FIXSALE is the
average ratio of the change in averagegross fixed assets to annual
sales, and is calculated as the average of the 1999 and 2000 ratios;
DEBTASSET is the average ratio of debt to the book value of assets, and
is calculated as the average of the 1999 and 2000 ratios; MKTRISK is
the [beta] coefficient obtained from a regression of monthly stock
returns on monthly market returns; FIRMRISK is the standard error of
the [beta] estimate obtained as the measure of MKTRISK; and, ASSET is
the average book values of assets across 1999 and 2000.

              Q     PRATE   RAWMSO     MSO   RAWTOP5    TOP5   RDSALE

Q            1.00
PRATE       -0.09    1.00
RAWMSO       0.02    0.09    1.00
MSO          0.04    0.23    0.81     1.00
RAWTOP5      0.01    0.01    0.28     0.00     1.00
TOP5         0.00    0.03    0.00    -0.16     0.80     1.00
RDSALE       0.33   -0.05   -0.06     0.04    -0.15    -0.12    1.00
FIXSALE     -0.30    0.15    0.05     0.14    -0.09    -0.06    0.02
DEBTASSET    0.05   -0.30   -0.10    -0.21    -0.01    -0.05   -0.14
MKTRISK      0.40   -0.09   -0.02    -0.01     0.13     0.08    0.31
FIRMRISK     0.38   -0.45   -0.03    -0.05     0.13     0.05    0.20
ASSET       -0.13    0.06   -0.16    -0.26    -0.06    -0.03   -0.10

            FIXSALE   DEBTASSET   MKTRISK   FIRMRISK

Q
PRATE
RAWMSO
MSO
RAWTOP5
TOP5
RDSALE
FIXSALE       1.00
DEBTASSET    -0.70       1.00
MKTRISK      -0.10      -0.08       1.00
FIRMRISK     -0.25       0.18       0.74       1.00
ASSET         0.06       0.16      -0.16      -0.21

Table 6
Regression Results for the Demsetz and Villaonga (2001) Replication
Where Performance is Measured by Average Tobin's Q (9)

The table below presents results for regressions (1) through (2) using
Q as the measure of firm performance. Q is defined as the average of
annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs are
calculated as [(year-end book value of debt + year-end market value of
equity) / year-end book value of assets]. Results are included for
ordinary least squares ('OLS') and 2-stage ordinary least squares
('2-SLS') model fits. t-statistics are included in parentheses below
coefficient values. The notation is defined as follows: Q is the
average of annual Tobin'ss Q values for 1999 and 2000. Annual Tobin's
Qs are calculated as [(book value of debt + market value of equity)/
book value of assets]; MSO is log (RAWMSO / (100 - RAWMSO)), where
RAWMSO is the average year-end percentage of ordinary shares owned by
top management and the board calculated over 1999 and 2000; TOPS is log
(RAWTOP5 / (100 - RAWTOP5)), where RAWTOP5 is the percentage of
ordinary shares owned by the top 5 shareholders in 2000; RDSALE is the
average ratio of annual research and development expenditure to annual
sales, and is calculated as the average of the 1999 and 2000 ratios;
FIXSALE is the average ratio of the change in gross fixed assets to
annual sales, and is calculated as the average of the 1999 and 2000
ratios; DEBTASSET is the average ratio of debt to the book value of
assets, and is calculated as the average of the 1999 and 2000 ratios;
UTIL is a utility company indicator variable (which is equal to 1 if
the firm operates in the utility industry, and 0 otherwise); MED a
media company indicator variable (which is equal to 1 if the firm
operates in the media industry, and 0 otherwise); FIN is a finance
company indicator variable (which is equal to 1 if the firm operates in
the finance industry, and 0 otherwise); MKTRISK is the [beta]
coefficient obtained from a regression of monthly stock returns on
monthly market returns; FIRMRISK is the standard error of the [beta]
estimate obtained as the measure of MKTRISK; and, ASSET is the average
book values of assets across 1999 and 2000.

                       Performance
                    OLS           2SLS

(Intercept)     2.5167          2.5240
               (4.6685 ***)    (1.9839 **)
MSO             0.0909          0.0955
               (0.4995         (0.1277)
TOP5            0.0164          0.0187
               (0.0338)        (0.0308)
RDSALE          2.9792          2.9794
               (3.4825 ***)    (3.4774 ***)
FIXSALE        -0.2437         -0.2436
              (-3.9313 ***)   (-3.7890 ***)
DEBTASSET      -3.1304         -3.1218
              (-1.9018 *)     (-1.4547)
UTIL            0.5746          0.5706
               (0.3965)        (0.3591)
MED             0.0775          0.0740
               (0.0913)        (0.0730)
FIN            -0.1934         -0.1986
              (-0.1725)       (-0.1432)
F-stat          4.1580 ***      4.1200 ***
Multiple        0.2406          0.2389
  [R.sup.2]
Adjusted        0.1827          0.1809
  [R.sup.2]

                        Ownership
                    OLS            2SLS

(Intercept)    -1.5146         -1.5144
              (-5.0459 ***)   (-5.0354 ***)
DEBTASSET      -1.2637         -1.2626
              (-1.9707 *)     (-1.9519 *)
UTIL            0.8804          0.8800
               (1.1687)        (1.1661)
MED             0.7866          0.7867
               (1.7347 *)      (1.7327 *)
FIN             1.0258          1.0266
               (1.7351 *)      (1.7265 *)
Q               0.0269          0.0253
               (0.5417)        (0.2073)
MKTRISK        -0.0958         -0.0947
              (-0.5296)       (-0.4810)
FIRMRISK        0.1573          0.1616
               (0.1186)        (0.1186)
ASSET          -0.0002         -0.0002
              (-2.4531 **)    (-2.4297 **)
F-stat          2.5110 **       2.4740 **
Multiple        0.1606          0.1586
  [R.sup.2]
Adjusted        0.0966          0.0945
  [R.sup.2]

Note: * significant at the 10% level;

** significant at the 5% level; and

*** significant at the 1% level.

Table 7
Regression Results for the Demsetz and Villaonga (2001) Replication
Where Performance is Measured by Average Profit Rate

The table below presents results for regressions (1) through (2) using
PRATE as the measure of firm performance. PRATE is defined as the
average of annual profit rates for 1999 and 2000. Annual profit rates
are calculated as (net income / book value of equity). Results are
included for ordinary least squares ('OLS') and 2-stage ordinary least
squares ('2-SLS') model fits. t-statistics are included in parentheses
below coefficient values. The notation is defined as follows: PRATE is
the average of annual profit rates for 1999 and 2000. Annual profit
rates are calculated as (net income / book value of equity); MSO is log
(RAWMSO / (100 - RAWMSO)), where RAWMSO is the average year-end
percentage of ordinary shares owned by top management and the board
calculated over 1999 and 2000; TOP5 is log (RAWTOP5 / (100 - RAWTOP5)),
where RAWTOP5 is the percentage of ordinary shares owned by the top 5
shareholders in 2000; RDSALE is the average ratio of annual research
and development expenditure to annual sales, and is calculated as the
average of the 1999 and 2000 ratios; FIXSALE is the average ratio of
the change in gross fixed assets to annual sales, and is calculated as
the average of the 1999 and 2000 ratios; DEBTASSET is the average ratio
of debt to the book value of assets, and is calculated as the average
of the 1999 and 2000 ratios; UTIL is a utility company indicator
variable (which is equal to 1 if the firm operates in the utility
industry, and 0 otherwise); MED a media company indicator variable
(which is equal to 1 if the firm operates in the media industry, and 0
otherwise); FIN is a finance company indicator variable (which is equal
to 1 if the firm operates in the finance industry, and 0 otherwise);
MKTRISK is the [beta] coefficient obtained from a regression of monthly
stock returns on monthly market returns; FIRMRISK is the standard error
of the [beta] estimate obtained as the measure of MKTRISK; and, ASSET
is the average book values of assets across 1999 and 2000.

                        Performance
                    OLS            2SLS

(Intercept)     0.6672         -0.0346
               (4.3601 ***)   (-0.0956)
MSO             0.0988         -0.3428
               (1.9122 *)     (-1.6094)
TOP5            0.0189         -0.2019
               (0.1367)       (-1.1672)
RDSALE         -0.2936         -0.3080
              (-1.2091)       (-1.2617)
FIXSALE        -0.0209         -0.0312
              (-1.1891)       (-1.7005 *)
DEBTASSET      -1.3845         -2.2219
              (-2.9630 ***)   (-3.6336 ***)
UTIL            0.2380          0.6330
               (0.5786)        (1.3981)
MED             0.0803          0.4176
               (0.3331)        (1.4445)
FIN            -0.4127          0.0834
               (-1.2966)       (0.2111)
F-star           2.4280 **      2.2760 **
Multiple         0.1561         0.1478
  [R.sup.2]
Adjusted         0.0918         0.0829
  [R.sup.2]

                        Ownership
                    OLS           2SLS

(Intercept)    -2.0347         -1.4292
              (-5.8908 ***)   (-1.2551)
DEBTASSET      -1.0242         -1.2796
              (-1.6420)       (-1.6177)
UTIL            0.9072          0.8689
               (1.2463)       (-1.1471)
MED             0.8924          0.7713
               (2.0291 **)     (1.5292)
FIN             1.3012          0.9982
               (2.2497 **)     (1.2361)
PRATE           0.5668         -0.0895
               (2.7872 ***)   (-0.0752)
MKTRISK        -0.2637         -0.0477
              (-1.4313)       (-0.1110)
FIRMRISK        2.4408         -0.1189
               (1.6249)       (-0.0246)
ASSET          -0.0002         -0.0002
              (-2.4899 **)    (-2.4629 **)
F-star          3.6220 ***      2.4690 **
Multiple        0.2163          0.1583
  [R.sup.2]
Adjusted        0.1566          0.0942
  [R.sup.2]

Note: * significant at the 10% level;

** significant at the 5% level; and

*** significant at the 1% level.

Table 8
Results of the General Non-linear Model Specification

The table below presents results for regression (3) using Q and PRATE
as alternate measures of firm performance. Q is defined as the average
of annual Tobin's Q values for 1999 and 2000. Annual Tobin's Qs are
calculated as [(year-end book value of debt + year-end market value of
equity) / year-end book value of assets]. PRATE is defined as the
average of annual profit rates for 1999 and 2000. Annual profit rates
are calculated as (net income / book value of equity). t-statistics are
included in parentheses below coefficient values. The notation is
defined as follows: Q is the average of annual Tobin's Q values for
1999 and 2000. Annual Tobin's Qs are calculated as [(book value of debt
+ market value of equity)/book value of assets]; RDSALE is the average
ratio of annual research and development expenditure to annual sales,
and is calculated as the average of the 1999 and 2000 ratios; FIXSALE
is the average ratio of the change in gross fixed assets to annual
sales, and is calculated as the average of the 1999 and 2000 ratios;
DEBTASSET is the average ratio of debt to the book value of assets, and
is calculated as the average of the 1999 and 2000 ratios; UTIL is a
utility company indicator variable (which is equal to 1 if the firm
operates in the utility industry, and 0 otherwise); MED a media company
indicator variable (which is equal to 1 if the firm operates in the
media industry, and 0 otherwise); FIN is a finance company indicator
variable (which is equal to 1 if the firm operates in the finance
industry, and 0 otherwise); RA WMSO is the average year-end percentage
of ordinary shares owned by top management and the board calculated
over 1999 and 2000; [RAWMSO.sup.2] is equal to RAWMSO squared;
[RAWMS.sup.3] is equal to RAWMSO cubed; [KNOT5.sup.3] is equal to max
[(RAWMSO-5,0).sup.3]; and, [KNOT25.sup.3] is equal to max
[(RAWMSO-25,0).sup.3].

                          Q             PRATE

(Intercept)            2.2855          0.4075
                      (4.0285 ***)    (2.4505 **)
RDSALE                 3.0132         -0.2958
                      (3.4794 ***)   (-1.1651)
FIXSALE               -0.2399         -0.0205
                     (-3.9673 ***)   (-1.1571)
DEBTASSET             -3.0575         -1.4375
                     (-1.9216 *)     (-3.0822 ***)
UTIL                   0.5027          0.2440
                      (0.3494)        (0.5786)
MED                    0.0122          0.1437
                      (0.0145)        (0.5807)
FIN                    0.2509         -0.3341
                      (0.2171)       (-0.9860)
RAWMSO                -0.5690          0.0823
                     (-0.8151)        (0.4022)
[RAWMSO.sup.2]         0.2063         -0.0184
                      (1.1624)       (-0.3540)
[RAWMSO.sup.3]        -0.0156          0.0013
                     (-1.2449)        (0.3564)
[KNOT5.sup.3]          0.0161         -0.0013
                      (1.2677)       (-0.3598)
[KNOT25.sup.3]        -0.0006          0.0000
                     (-1.9815 **)     (0.5246)
F-stat                 3.6220 ***      1.6100
Multiple [R.sup.2]     0.2809          0.1479
Adjusted [R.sup.2]     0.2034          0.0560

Note: * significant at the 10% level;

** significant at the 5% level; and

*** significant at the 1% level.

(1.) Demsetz and Villalonga (2001, p211).

(2.) Demsetz (1983, p. 387).

(3.) Morck, Schleifer and Vishny (1988) explain that leverage also captures the value of corporate tax shields that could result in higher values of performance indicators, including Tobin's Q. However, as a result of the imputation tax credit system introduced into Australia in 1987, corporate tax effectively a prepayment of personal taxes and, therefore, the value of corporate leverage-induced tax shields is zero in the Australian context.

(4.) Industry classifications were based on the Global Industry Classification Standard ('GICS') as at 11th October 2002.

(5.) Ibid.

(6.) Beta estimates were calculated using 4 years of monthly return figures from July 1998 to June 2002. The Risk Management Service in the Centre for Research in Finance at the AGSM provides two types of beta estimates: one calculated using a standard ordinary least squares approach; and, the other calculated using a Scholes and Williams (1977) approach to account for the existence of thin trading. As betas were calculated using monthly observations, it was decided that it was not necessary to use estimates that had been adjusted for thin trading.

(7.) Smith and Kohn (1996).

(8.) Industry classifications were based on the Global Industry Classification Standard ('GICS') for Australia as at 11th October 2002.

(9.) Regression analysis revealed several leverage points of concern. Models were refit excluding these observations, however, results did not differ substantially.

References

Berle, A. & Means, G. 1932, The Modern Corporation and Private Property, The Macmillan Company, New York.

Cho, M-H. 1998, 'Ownership structure, investment and the corporate value: An empirical analysis', Journal of Financial Economics, vol. 47, pp. 103-21.

Craswell, A.T., Taylor, S.L. & Saywell, R.A. 1997, 'Ownership structure and corporate performance: Australian evidence', Pacific-Basin Finance Journal, vol. 5, pp. 301-23.

Demsetz, H. 1983, 'The structure of ownership and the theory of the firm', Journal of Law and Economics, vol. 26, pp. 375-90.

Demsetz, H. & Lehn, K. 1985, 'The structure of corporate ownership: Causes and consequences', Journal of Political Economy, vol. 93, pp. 1155-77.

Demsetz, H. & Villalonga, B. 2001, 'Ownership structure and corporate performance', Journal of Corporate Finance, vol. 7, pp. 209-33.

Hermalin, B. & Weisbach, M. 1991, 'The effects of board composition and direct incentives on firm performance', Financial Management, vol. 20, pp. 101-12.

Himmelberg, C., Hubbard, R.G. & Palia, D. 1999, 'Understanding the determinants of managerial ownership and the link between ownership and performance', Journal of Financial Economics, vol. 53, pp. 353-84.

Holderness, C., Kroszner, R. & Sheehan, D. 1999, 'Were the good old days that good? Evolution of managerial stock ownership and corporate governance since the great depression', Journal of Finance, vol. 54, pp. 435-69.

Loderer, C. & Martin, K. 1997, 'Executive stock ownership and performance: Tracking faint traces', Journal of Financial Economics, vol. 45, pp. 223-55.

McConnell, J. & Servaes, H. 1990, 'Additional evidence on equity ownership and corporate Value', Journal of Financial Economics, vol. 27, pp. 595-612.

Morck, R., Shleifer, A. & Vishny, R. 1988, 'Management ownership and market valuation: An empirical analysis', Journal of Financial Economics, vol. 20, pp. 293-315.

Scholes, M. & Williams, J. 1977, 'Estimating betas from non-synchronous data', Journal of Financial Economics, vol. 5, pp. 309-27.

Smith, M. & Kohn, R. 1996, 'Nonparametric regression using Bayesian variable selection', Journal of Econometrics, vol. 75, pp. 317-44.

Emma Welch, School of Finance and Applied Statistics, Australian National University, Canberra, ACT, 0200, AUSTRALIA; Email: [e-mail]

Comments and suggestions made by Richard Heaney, Michael Martin and Tom Smith are gratefully acknowledged.

(Date of receipt of final transcript: May 29, 2003. Accepted by Garry Twite & Doug Foster, Area Editors.)