Sampling Strategy

Kumar (2011) states that the purpose of sampling in quantitative research is to draw inferences about the population from which the sample was selected, therefore it is important to select an unbiased sample that is a good representation of the population. The sample chosen for this study is the member companies of the FTSE 350 index between two periods, namely end of December 2004 to end of December 2014. The FTSE 350 is the largest 350 companies from various industries listed on the London Stock Exchange by market capitalisation. There are various reasons for selecting the FTSE 350 index as the sample for this study. First, the FTSE 350 index covers a significant percentage of the London Stock Exchange market capitalisation making it a good representation of the population of large UK companies (FTSE Group, 2016). Second, the FTSE 350 companies are all subject to the same corporate governance provisions provided by the UK Corporate Governance Code and this allows the study to construct the board index used in this research. A single country is

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selected in order to avoid differing legal structures and this is consistent with Zattoni and Van Ees (2012) who suggest that this is beneficial because legal and cultural institutions have a strong impact on governance phenomena and mechanisms. Lastly, this study selects the FTSE 350 member companies as its sample because previous corporate governance research in other countries, such as the USA, has examined large companies listed on indexes, which are comparable to the sample of this research (Erhardt et al., 2003; Harford, Mansi, & Maxwell, 2012; Yermack, 1996). The majority of studies in both the upper echelons and corporate governance fields have been largely based in the USA. Zalewska (2014) observes that many of the previous studies on board composition have also largely focused on the USA in comparison with other countries. Therefore, the choice of sample will contribute to the existing body of literature in the corporate governance arena by focusing on the FTSE 350 companies in the UK. Board diversity and diversity in the workplace has become a growing issue of interest in politics, codes of best practice and society at large in the UK. For instance, the Financial Reporting Council, fairly recently, made changes to the UK Corporate Governance Code that now require listed companies to disclose and publish their diversity policies (Vinnicombe et al., 2015).

The period of study is between the years 2004 and 2014 in order to capture the evolving nature of UK board composition. When this study began, the most recent corporate governance code in the UK was the Corporate Governance Code 2014. As a result, the selected sample period encompasses the evolution of the codes of best practices in the UK, particularly from the Combined Code (2003) to the Corporate Governance Code 2014.⁵ According to Zattoni and Van Ees (2012) the analysis of longitudinal data samples is helpful in understanding the dynamics of governance mechanisms as corporate governance issues are in continuous evolution. Previous studies on board composition and performance have used time lags ranging from one to three years for dependent variables with no consensus as to which lag is best (Abdullah, Ismail & Nachum, 2016; Carter et al., 2010; Hitt et al., 2006; Jackling &

Johl, 2009). A meta-analysis of women on boards and firm performance, conducted

5 The researcher is aware that there now later editions of the code, namely the UK Corporate Governance Code 2016 and 2018. However, at the time when the research commenced and data was collected the most recent code was the UK Corporate Governance Code 2014.

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by Post and Byron (2015) found studies with effect sizes derived from firm performance were more positive for studies that used lags versus those that had no lags. Weir and Laing (2000) use a one-year time lag for their study and suggest that the lag between the change in governance structure of a firm and the effect on firm performance is particularly longer for accounting performance measures. Similarly, earlier research by Daily and Johnson (1997) confirmed that board composition was associated with financial performance two years later. Therefore, although the sample period for this study covers ten years, data for the independent variables is collected for the years 2004, 2006, 2008, 2010 and 2012. The performance measures and control variables are collected for the years, 2006, 2008, 2010, 2012 and 2014. This is in order to analyse differences over longer periods and to incorporate a two-year lag between the dependent and independent variables, as their effects will not likely be immediate (Daily & Johnson, 1997; Hitt et al., 2006).

Initially the sample consisted of all companies that were listed on the FTSE 350 index at any point during the sample period. The screening process required companies to meet the following criteria:

 The company had to be listed on the FTSE 350 index for a minimum of five consecutive years during the sample period.

 The company had to have data reported on the Bloomberg database or in publicly available annual reports.

Historical data on the FTSE 350 index company listings was gathered from the Bloomberg portal and the London Stock Exchange. Appendix A provides a full list of the screening process, including the companies that were included and excluded in the sample, and Table 4.2 provides a summary of the screening process.

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Table 4.2 Overview of Sample Screening

Criteria No. of

Companies

Initial sample 637

Exclude companies delisted from FTSE over sample period -413 Companies consistently listed on the FTSE 350 for a minimum of five

consecutive years between 2004 - 2014 ⁶

224

Exclude equity investment instruments -22

Exclude companies with no data reported on Bloomberg or available annual reports

-4

Final sample 198

A total of 224 companies met the first criteria and were listed on the FTSE 350 index for five consecutive years during the sample period. Companies listed as equity investment instruments were removed from the sample as their data is not available and thus did not meet the second criteria. This brought the final sample for this study to a total of 198 companies, of which 78 of the firms in the sample are listed on the FTSE 100. A noticeable change in the screening process was that many companies that were excluded from the sample either were delisted from the index in 2008 or only became listed in 2010 after the financial crisis of 2007/8. A total of 41 firms in the final sample were not listed on the FTSE 350 index in the year 2004 and this year has the lowest representation of the sample and makes the dataset an unbalanced panel.

Moreover, the inclusion of companies that have been consistently listed on the FTSE 350 creates survivorship bias, however, it also allows the researcher to observe changes in board diversity for different firms over time. Overall, the sample is a good representation of the population of the largest 350 companies in the UK in each given year.

6 This criterion was selected to ensure the companies that were included were listed on the FTSE 350 in the majority of the sample period, where they would all be liable to adhere to similar codes of best practices and regulatory requirements.

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Previous studies on corporate governance and performance have excluded financial firms from their sample for various reasons. The main reasons behind this exclusion have been that financial firms are heavily regulated; therefore, their corporate governance systems and corporate performance may be affected differently to other industries (Cheng, 2008; Weir, Laing & McKnight, 2002). In the first part of the analysis, this study includes financial firms because doing so increases the sample size, which may lead to better results. The study also controls for variations in sectors and industries in the statistical software package used for the analysis. In the second part of the analysis, this study groups the firms into industry sectors and analyses the data as an industry comparison of 16 industry sectors as classified by the Standard Industrial Classification (SIC) codes. Hiebl (2013) notes that contradictory results on certain upper echelons characteristics may be attributable to different industries as one size or structure may not fit all. For instance, Naranjo-Gil and Hartmann (2007) found a positive relationship between CEOs’ education and financial measurement systems whilst Burkert and Lueg (2013) did not find any significant relationship. However, Naranjo-Gil and Hartmann (2007) studied public hospitals whilst Burkert and Lueg (2013) studied large listed companies. The London Stock Exchange categorises companies into 41 different industries, however there are some industries into which none of the FTSE 350 companies fall and other industries that only have one or two companies under them. Therefore, the sample is broken down by industry according to SIC codes, which classify companies in industry sectors according to the economic activities that the companies are engaged in. Table 4.3 displays how the sample set is

broken down into 16 industries over the sample period.

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Table 4.3 Industry Breakdown of Sample Set

7 This represents the total number of companies in the sample for each given year over the sample period. The year 2004 had the lowest representation of the sample compared to other years.

Industry 2004 2006 2008 2010 2012 TOTAL

Accommodation, Food & Beverages Services 8 10 10 10 10 48

Banking 7 7 7 7 7 35

Business Support, Leasing, Employment, Public Administration Activities 6 12 12 12 12 54

Construction and Development of Buildings 17 20 20 20 20 97

Electricity, Gas, Water collection & Sewerage 4 5 5 5 5 24

Extraction of Crude Petroleum & Natural Gas 5 9 9 9 9 41

Financial Services, Auxiliary Services to Finance & Real Estate Activities 13 20 20 20 20 93

Insurance 6 8 8 8 8 38

IT, Media, Broadcasting & Publishing 6 8 8 8 8 38

Management Consultancy, Head Offices Activities, Architectural & Engineering Services 12 15 15 15 15 72

Manufacturing 32 36 36 36 36 176

Mining & Quarrying 7 7 7 7 7 35

Retail Sales, Gaming & Betting activities 12 15 15 15 15 72

Telecommunications 6 7 7 7 7 34

Transport 8 9 9 9 9 44

Wholesale Trade 8 10 10 10 10 48

TOTAL⁷ 157 198 198 198 198 949

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Table 4.3 displays a slight change in sample size in some industries and most changes in size occur between the years 2004 and 2006 where there is an increase in size. The most considerable change is seen in the financial services, auxiliary services to finance and real estate activities industry sector which had 13 companies in 2004 and 20 companies in 2006 onwards. Industry sectors with closely linked SIC codes and similar economic activities are merged together and this is displayed in Appendix B. The industries with the most companies in this sample set are the manufacturing, financial service and construction industry sectors. Overall, this study will have 949 firm year observations comprised of 16 industries over a 10-year period. Research conducted by Carpenter et al. (2004) observed it is important to examine the environment at the industry level because complex environments may require heterogeneous boards whilst homogenous boards may be more effective in stable industries and environments. Therefore, this study controls for industry dynamism which is further discussed in the next sections.