LAS PERSONAS CON DISCAPACIDAD

With the company name (COMPANY) and initial market value (MV) established through the creation of the sample, the next variable collected was the company’s Datastream code (DSCODE), used as a unique reference number for each company thereafter. The 3-digit (ICODE-3) and 1-digit (ICODE-1) industrial classification codes and industry description (ITYPE) were then recorded based on the company’s entry in the PWC Corporate Register in September, 1995. Table 3.9 shows the distribution of the sample companies over the six main industrial groupings. The majority of the companies are either service companies or general manufacturing companies, with these

two sectors alone accounting for approximately 59% of the sample. Note that no investment trusts are included in the sample.

Table 3.9: Number of Companies by Industry Sector Group, Time-Period: 1990- 1998, Sample: Top 460 London Stock Exchange Firms

Industry Sector Mineral Extraction General Manufacturers Consumer Goods Services Utilities Financials Total

1-digit Industry Code

1 2 3 4 6 7 Number of Companies 18 140 56 130 44 72 460

Next, the total number of ordinary shares in issue (EQUITY) of each company for each year over the period 1990-1998 was manually recorded from the September issues of the PWC Corporate Register (Companies Section). Similarly to top managers' equity, information regarding the company’s share capital was supplemented through other issues of the PWC Corporate Register and company accounts.

The rest of the company variables were collected from Datastream. Some of them were used in order to construct certain metrics applied in the current thesis. The construction of these metrics was another demanding and very important task of this thesis. As mentioned, for ease of exposition, the description of this task will be fully discussed in the chapters in which the variables were used. Instead, the current section provides a full explanation of these variables.

Accordingly, the return index (RI) was collected on the 1st January of each year for each company in order to compute an annual log shareholder return figure. A company’s

return index shows the growth in the share value and the value of the dividends. The relevant formula is:

RI, = RI_ *

r P, +D, '

V p t-1 J

where Pt= price on ex-date (i.e. the day dividend payments become certain), Pt.| = price on previous day and Dt = dividend payment associated with ex-date t.

Moreover, the unadjusted share price (UP) for each company on the 1st January of each year was selected in order to calculate the value o f ordinary stock holdings owned by executives. This is the closing price, which has not been historically adjusted for bonus and rights issues. This figure, therefore, represents actual or raw prices as recorded on the day.

The rest of the company specific variables are company accounts variables. For each of them a full explanation along with the Datastream item number is given. The variables include:

• Earnings before interest and tax (EBIT-Datastream item 1300): this is calculated by taking the pre-tax income and adding back only the total interest expense on debt. • Total assets employed (ASSETS-Datastream item 391): it is defined as the sum o f

tangible fixed assets, intangible assets, investments, other assets, total stocks and work in progress, total debtors and equivalent, cash and cash equivalents, minus current liabilities.

• Total new fixed assets (INVESTMENT-Datastream items 435 & 1024)15: this is the total of fixed assets purchased, including assets from subsidiaries acquired.

• Depreciation (DEPRECIATION-Datastream item 136): this includes provision for amounts written off, and depreciation of tangible fixed assets.

• Operating profit -Adjusted (OPROFITA-Datastream item 137): this is net profit derived from normal activities of the company after depreciation and operating provisions. Adjustments include items of an exceptional nature, which do not form part of the company’s normal trading activities.

• Total sales (SALES-Datastream item 104): the amount of sales of good and services to third parties relating to the normal industrial activities of the company.

• Total loan capital (DEBT-Datastream item 321): this represents the total loan capital repayable after one-year. It includes debentures, bonds, convertibles, and “debt like” hybrid financial instruments.

• Total plant and machinery - gross (GVP-Datastream item 328): this relates to the gross book value of plant, machinery, motor vehicles, equipment, furniture, fittings etc.

• Total land and buildings - gross (GVB-Datastream item 327): the gross book value of all plant and machinery (i.e. freehold, leasehold etc.)

• Total plant and machinery - net (NVP-Datastream item 699): this shows total plant and machinery, net of depreciation.

• Total land and buildings - net (NVB-Datastream item 698): this shows total land and buildings, net of depreciation.

IS

Finally, there were two variables that are neither company specific nor executive specific. These include the total UK capital stock in current prices (CSCP) and the total UK capital stock in 1995 prices (CS95). Both o f these variables were used in Chapter 6 in order to construct the net capital stock at replacement cost and were collected by contacting directly the office for National Statistics in London.

3.4 Concluding Remarks

This chapter has detailed the construction of the study’s data set. As highlighted in the above sections, the variable collection process was particularly labour-intensive and time-consuming. It enabled, however, the advancement of prior literature in three main ways. Firstly, it resulted in the construction of a comprehensive and unique data set on the composition of the top management teams, drawn on the top 460 UK listed companies by market capitalisation over the period 1990-1998. This in turn has two valuable implications: a) it allowed a more precise identification of the company’s leading executive than before, and b) it enabled - for the first time in the UK - the modelling of Chairman turnover. Both of the above are substantial contributions to the mapping of senior management departures in the UK.

Secondly, it generated a number of enlightening information regarding important characteristics of the UK top management teams (e.g. the existence of combined titles, stock-based compensation etc.) The availability of panel data, in particular, enabled the description of trends and developments in UK top management teams. Finally, due to the quality of the data, this thesis was able to appropriately classify executive departures in order to perform strong tests o f the hypotheses under investigation. Accordingly, the thesis was able to use a) a much less noisy measure of forced departures (Chapters 4, 5

and 6), and b) a much more accurate measure of planned departures (Chapter 6) than previous studies16. The classification strategy itself is a key issue in executive turnover studies.

The structure of the data set as well as the quality of the data also made it possible to accurately match the timing of management departures with the appropriate firm performance metrics. That is of particular importance in this study. Management changes, as explained above, were identified by comparing the composition of top management teams across years. The annual period, however, is not a calendar year, but instead September to September. This is because the primary data source (i.e. the PWC Corporate Register) was only published semi-annually in pre-1994 and thus an analysis by calendar year was not possible. That means, that each annual period overlaps two different calendar years.

Therefore, a director who was in the top management team in September 1993, but not in September 1994, may have left in either the calendar year 1993 or 1994. In order to examine whether prior year’s performance has led to the turnover of this manager one would need the exact leave date (i.e. if he/she left in 1993 then prior year’s performance refers to 1992 whilst if he/she left in 1994 prior year’s performance refers to 1993). Therefore, as mentioned, an additional variable (i.e. the actual date of the departure) was collected to further improve the accuracy and consistency of the data. Consequently, the current analysis was able to locate the actual date of the turnover event and match it against the appropriate annual performance measure.

It should be noted that despite every effort a small level of noise in the forced departure variable is unavoidable.

Though comprehensive, the data set is not without its weaknesses. Although as detailed above, every effort was made to match the timing events, at times it does remain an approximation. The main limitation thus, stems from the inability to precisely identify the financial year end date of the sample companies. Datastream does not directly provide this information whilst the annual reports for a major part of the sampled firms - especially during the early years - were not readily available. Consequently, the current study assumes that the majority of the companies have a December end year whilst a smaller fraction has a March end year. However, the error is not likely to be significant. Drawn on a sample of 510 UK quoted companies in 1997, Sadler (1999) shows that 47% of the companies have a December year end with a further 20% having a year end of March.

Bearing in mind that there is a lag period between the date companies produce their annual reports and the date Datastream receives and disseminates this information, all accounting variables used in this study (e.g. EBIT, depreciation etc.) were collected as in August of each year. Hence, for the majority of the sample firms with a December year end all variables (i.e. the turnover event, shareholder returns and accounting variables) were perfectly matched whilst for those companies with a March end year there is a three-month lag only in the accounting variables. The timing of the variables remains an issue for only those companies that have an end year other than December or March, such as June or February. But as these companies are very likely to account for about one quarter of the entire sample, the error is expected to be only minor.

Up to now Chapters 2 and 3 have prepared the reader for the empirical part of the current thesis by discussing the general conceptual background of the analysis.

reviewing prior empirical work, explaining the construction of the data set, and drawing the profile of UK top management teams. The main analysis starts with the following chapter, which empirically explores the association between top management changes and firm performance as well as the circumstances under which poor performance can lead to a top management job separation.

Most Senior Executive Turnover, Firm