The Econometric Model

Similarly to previous studies (Weisbach et al. 1988; Conyon 1998; Huson et al. 2001), the following probit model was estimated where <t> is the standard cumulative normal distribution with zero mean and unit variance:

Pr (yi* 0 / XiP)=<t> (Xip)

When the dependent variable assumes discrete values, a non-linear regression model (i.e. logit or probit) as opposed to the linear probability regression model is most often preferred (Gujarati, 1998). This is mainly because a linear regression model assumes that the conditional probabilities increase linearly with the values of explanatory variables. However, the probabilities will tend to taper off as the values of the explanatory variables increase or decrease indefinitely. Consequently, what is needed is a probability model whose error term (eu) follows the S-shaped feature of the cumulative distribution function. Two such models are the logit and probit models, which both guarantee that the estimated probability of the event (in this case the probability of the executive departure) will lie in the 0-1 range (Gujarati, 1998).

In the above probit model, the term x,[3 is the probit score, where x contains forcing variables and (3 is the population vector to be estimated by maximum likelihood methods. Specifically, the x matrix contains proxies for stock-based and accounting-

based company performance, managerial stock ownership, company size, and managerial age. The term y is an indicator variable relating to the probability of top management departure. In particular, probit regressions were estimated under three definitions of a management change: a) all Most Senior Executive changes, b) forced Most Senior Executive changes and c) non-forced Most Senior Executive changes18. A zero (0) indicates a negative outcome, whereas a one (1) represents a positive outcome, i.e. if there is a change in the Most Senior Executive in a given year. Finally, all probit models included specific industry and time effects.

The following sections consider in details the left-hand side (LHS) and the right-hand side (RHS) variables of the model.

The LHS Variables

The basic dependent variable in this study is the change in the Most Senior Executive. This is an indicator variable equal to one if the Most Senior Executive is not disclosed in the firm’s top management team in year t+1, and zero otherwise. As highlighted in Chapter 2, title re-assignments were not treated as changes, since they are less likely to be associated with changes in the firm performance. Identifying the leading company executive in UK firms was a complicated task since the title “Chief Executive Officer” has only comparatively recently been used to signal the top corporate position. Other titles such as Chairman and Managing Director were also used - especially in earlier periods (see Table 3.1-Chapter 3). Clearly, given the complexity in constructing the data it is important to understand what the Most Senior Executive refers to. Accordingly, the

18 An alternative approach would be to estimate a multinomial logit regression with three turnover outcomes: 0 =no change, l=forced change and 2=non-forced change. Comparing the two models, it was found that results were qualitatively consistent

Most Senior Executive in each company for each year was taken to be the CEO if such a role existed. When no CEO existed the Most Senior Executive was taken to be either the Executive Chairman or the group Managing Director.

Out of the 711 total top executive departures identified in Chapter 3, 318 refer to MSE changes. Moreover, all Most Senior Executive departures were grouped into forced and non-forced according to the classification strategy described in Chapter 3. This process resulted in a total of 135 MSE forced departures and 180 MSE non-forced ones (a total of 315 observations)19.

The number of firm observations, all, forced and non-forced MSE changes per year and per company is presented in Table 4.1. As indicated, there is no particular time-series trend in the actual turnover rate under all definitions of turnover. Moreover, there are no big fluctuations in all three turnover rates from year to year. The only exception are years 1990 and 1998 that have significantly lower turnover rates, compared to the rest of the sample years, since they represent a six-month period instead o f a full twelve- month period. Finally, the turnover rate for the leading executives in UK companies is on average 9.4% whilst forced and non-forced MSE departures average 4% and 5.3% respectively.

9 The three MSE departures - for which no information was found - were excluded from the sample construction, but included in the regression analysis when the dependent variable was all Most Senior Executive changes.

Tabic 4.1: Sample Firms, All, Forced and Non-Forced MSE Changes by Year, Time-Period: 1990-1998, Sample: Top 460 London Stock Exchange Firms

-, Number of . . . . Year ~ All changes

firms Forced changes Non-Forced changes 1990 370 13(3.5%) 5 (1.4%) 7(1.9%) 1991 380 37 (9.7%) 18 (4.7%) 18(4.7%) 1992 376 47(12.5%) 20 (5.3%) 27 (7.2%) 1993 380 43 (11.3%) 16(4.2%) 27 (7.1%) 1994 384 34 (8.9%) 16(4.2%) 18(4.7%) 1995 391 42(10.7%) 14(3.6%) 28 (7.2%) 1996 380 37 (9.7%) 14 (3.7%) 22 (5.8%) 1997 373 45 (12.1%) 20 (5.4%) 25 (6.7%) 1998 355 20 (5.6%) 12 (3.4%) 8 (2.3%) Total 3389 318 (9.4%) 135 (4.0%) 180 (5.3%)

Table 4.2 presents the reasons given by companies for the job separation as well as the number of forced and non-forced MSE departures by reason.

Table 4.2: Forced and Non-Forccd Most Senior Executive Changes by Stated Reason as Reported in the Financial Times, Time-Period: 1990-1998, Sample: Top

460 London Stock Exchange Firms

Reasons Forced changes Non-Forced . _ . ,Total changes

Retired and left board 10 78 88

Retired and stayed on board 1 11 12

Normal succession 0 11 11

Death 0 5 5

Health/lllness 0 15 15

Policy/Personality disagreement 16 0 16

Poor performance 47 0 47

Personal reasons/Other interests 8 8 16

Take position in another firm 1 16 17

Fired 1 0 1

Assume other position in firm 3 11 14

T ake-over/Merger 0 12 12

De-merger 0 6 6

Scandal 8 0 8

Other 10 7 17

No clear reason reported 19 0 19

Limited Information 11 0 11

Total 135 180 315

As expected the most commonly reported reason as well as the majority of the non- forced departures is retirement; 100 cases (31.7% of the total sample and 49.4% of the non-forced departures). Note that there were 11 cases for which information available was limited. Initially they were grouped as forced but because there is a possibility of misclassification in this case, the current analysis performed an additional test to examine the robustness of the results. This will be fully described at a later stage.

The RHS Variables

In order to test the poor-performance hypothesis two main measures of company performance were used in this study. These include (variable coding as well as Datastream item or data type shown in brackets):

a) Company stock returns (SHR): The return on the company’s stock was calculated as the log of (RI,+i/RIt), where RI stands for the company's return index on 1st January as discussed in Chapter 3.

b) Accounting returns (EBIT): In an efficient market stock prices anticipate the future benefits of the possibility of CEO dismissal and therefore tend to increase, as the capital market becomes aware o f new avenues for management improvement. As a result, they may under-estimate the monitoring role of internal disciplining devices. Accounting-based measures, on the other hand, are more stable and are not vulnerable to speculative or exogenous shocks (although a counter argument could be that accounting-based measures may be both susceptible to managerial

manipulation and endogenous)20. Accounting earnings may, therefore, play a significant role in the process of internal governance of companies. Company accounting returns were calculated as the level of accounting earnings before interest and tax (1300) standardised by the book value o f the firm’s total assets in the beginning of the year (391) to control for size differences.

In addition to the company's own performance measures, the study tested the sensitivity of the results by using relative performance measures. These include: a) industry adjusted stock returns (RSHR) and b) industry adjusted accounting returns (REBIT). Since the construction of the relative performance measure is the same for both stock returns and accounting returns the discussion here will focus on one of them. Accordingly, the industry adjusted stock returns were measured as follows:

a) The median value of stock returns for all firms in the same one-digit SIC industry and for each year was separately calculated.

b) Company's own stock returns were then adjusted for each year by subtracting the equivalent median value (i.e. the median value of the industry in which the firm was active). Hence, a positive difference indicates that the company outperforms the industry whilst the opposite holds if there is a negative difference.

Moreover, lagged firm performance instead o f current performance was used. This was chosen because of two main reasons. Firstly, because of potential endogeneity problems. Whilst this year's poor firm performance may lead to an executive departure,

20 Peasnell et al. (1999), for example, provide evidence that when the proportion of non-executives is high, managers are less likely to make income-increasing accruals to avoid reporting earnings losses or declines.

such an event may also affect current performance. In this case, firm performance is no longer exogenous. Secondly, there is generally a time lag between poor firm performance and the board’s decision to remove inefficient managers. Of course, it is plausible that the time lag could be relatively short, in which case contemporaneous performance - measured by quarterly data - could also be incorporated in the model. Given the unavailability of quarterly data in the UK, annual lagged performance measures in years t-1 and t-2 were used. Since previous studies indicate that firm performance more than two years prior to the unit of observation is not a significant explanator of executive turnover (Warner et al. 1988; Hadlock and Lumer 1997), this study did not include lagged performance measures beyond year t-2.

In order to test the entrenchment hypothesis, managerial ordinary stock ownership (STAKE) was operationalised as the fraction of ordinary shares owned by the Most Senior Executive. Note that managerial option holdings were not included in the analysis, since they do not entitle executives to additional voting rights and hence, are not correlated with power. Several studies argue that company size and CEO age are potentially important predictors of the turnover possibility (Warner et al. 1988; Jensen and Murphy 1990). This analysis caters for these effects by incorporating in the model size and age as control variables. Accordingly, size (SIZE) was measured as the log of the market value of the company (MV) and managerial age (AGE) was calculated for each year and each Most Senior Executive based on his/her birth date.

Finally, a set of indicator variables allocating companies to their 1-digit industry group (INDUSTRY EFFECTS) as well as set of indicator variables allocating observations to

one of the nine sample years, i.e. 1990-1998, (TIME EFFECTS) were also included in all regression models.

Descriptive statistics of the distributions of the independent variables are given in Table 4.3.

Table 4.3: Summary Statistics of All Independent Variables, Time-Period: 1990- 1998, Sample: T op 460 London Stock Exchange Firms

Variable Obs. Mean Std.

D ev. Min. Max.

I s' Quartile Median 3 rd Quartile SHR 3296 0.065 0.407 -3.993 1.760 -0.101 0.100 0.297 EBIT 2994 0.114 0.126 -1.217 2.458 0.066 0.110 0.161 RSHR 3296 -0.028 0.373 -3.964 1.583 -0.159 0.012 0.161 REBIT 2994 0.004 0.123 -1.346 2.309 -0.039 0.000 0.044 MV 3366 1981.4 4441.9 0.28 74902.8 265.76 604.72 1771.25 SIZE (log MV) 3366 6.530 1.432 -1.272 11.223 5.582 6.404 7.479 AGE 3356 53.64 6.68 31 81 49 54 58 STAKE 3314 0.023 0.089 0.000 0.894 0.000 0.003 0.028

As indicated, the size variable - initially measured as the market value o f the company - is particularly skewed. Consequently, size was log-transformed and the distribution of the transformed variable approaches normal. The mean company shareholder return and accounting return is 0.065 and 0.114 respectively whilst industry adjusted stock returns and accounting returns are on average -0.028 and 0.004 respectively. The mean company market value is £ 1981m. Finally, Most Senior Executives are on average 54 years old and hold 2.3% of the company's total equity.

To conclude this section, Tables 4.4 and 4.5 present the mean standard Pearson correlations between all continuous variables, where firm performance is measured by company’s own returns and company's relative returns respectively.

Table 4.4: Standard Pearson Correlations of Independent Variables (Company's Own Performance), Time-Period: 1990-1998, Sample: Top 460 London Stock

Exchange Firms

SHR,., SHR, 2 EBIT,., EBIT,.2 SIZE AGE STAKE SHR,., - SHR,2 0.06 - EBIT,., 0.27 0.23 - EBIT.2 0.09 0.24 0.54 - SIZE 0.25 0.18 0.09 0.07 - AGE 0.00 0.00 -0.01 -0.01 0.13 - STAKE 0.05 0.03 0.06 0.06 -0.16 0.08 -

Table 4.5: Standard Pearson Correlations of Independent Variables (Company's Relative Performance), Time-Period: 1990-1998, Sample: Top 460 London Stock

Exchange Firms

RSHR,.| RSHR.,2 REBIT,., REBIT,.2 SIZE AGE STAKE RSHR..I - RSHR.,2 0.13 - REBIT,., 0.30 0.25 - R E B IT ,2 0.12 0.27 0.51 - SIZE 0.24 0.18 0.11 0.09 - AGE 0.00 -0.00 -0.01 -0.01 0.13 - STAKE 0.06 0.04 0.05 0.05 -0.16 0.08

As shown in the above Tables, there is not a large degree of association between the study's independent variables, with the exception of the first and second years' accounting returns whose correlation is relatively high (approximately 0.5). Accounting returns, however, are hypothesised to jointly determine the CEO turnover likelihood.