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Corporate Governance in an International Context

Corporate Governance in an International Context Introduction It is a widely known fact that the board of directors of a firm determines not only its success but in corporate governance, the board of directors is at the apex of the internal control system and their structure and size largely determines the performance of the given firm (Jensen, 1993, pg. 862). The board typically has a legal authority to hire and fire employees and is involved in decision-making, monitoring and safeguarding the capital (Fama amp. Jensen, 1983, pp. 301-325). The purpose of this paper will try to link the structure and composition of a board with the overall performance of firms by conducting an empirical research using a sample of 30 UK listed companies from Morningstar Company Intelligence. The method of data collection employed in this detailed analysis is random sample of thirty out of 1779 UK listed companies that were selected from the Morningstar Company Intelligence. As mentioned earlier, besides broad focus on the board of directors, the study included specific analysis of important aspects of companies, such as turnover, ROCE, EPS, number of employees, board size, percentage of non-UK activities turn over, number of non-executive directors, CEO/chair duality, number of female directors and international directors. Analysis of this information was very imperative in fulfilling objectives of the research. Subsequently, we utilized the tool of SPSS to acquire descriptive statistics of the raw data along with doing descriptive statistics, correlation and regression analysis that will be very effective in presenting the data in understandable manner.
Empirical research is supposed to state a certain problem or a research question and then come up with theories and assumption. It is important to first design the research and after the methodology is determined, random sampling is employed so that the data can be gathered and analyzed.
A sample is a smaller (but hopefully representative) collection of units from a population used to determine truths about that population (Field, Miles amp. Field, 2012). Sampling is important because it saves the time, energy and resources and gives results that can be calculated mathematically and are accurate.
Probability sampling is a technique in which all elements e.g. people, families etc. have an opportunity to be included in the sample and the probability that any of them can be included mathematically. Simple random is a type of this technique and it selects the target population from the sampling frame in a completely random fashion. It is the least biased of all the other methods because there is no subjectivity and each member of the total population has an equal chance of being selected (RGS, 2013). If all the members of the population have an equal chance of being selected, this method of data collection is the most ideal because it is highly representative. It can only work, however, when the population is small, available, and homogenous because each member or subset has to have an equal probability of being selected for the sample. Each element is assigned a number and a table of random numbers or a lottery system selects the sample. Estimates are very easy to calculate in this type of a technique. For example, in a population of 500 people, each person has 1/500th of a chance of selection.
However, simple random sampling is not practical if the population is large as the lists of all the members would be hard to locate making the method inaccurate. Minority sub-groups might also be insufficient in the sampling frame to give them an equal chance of being selected and accounted for. The sampling frame derived for such a research technique usually comes from Registrar Office lists or a phonebook but for large populations, this might prove too cumbersome. A single researcher cannot possibly know the exact size of the parent population and cannot promise that each element would have the exact probability of selection.
The table 1 below summarizes the descriptive statistics of the variables used in the analysis, which shows the mean, median, minimum and maximum value, standard deviation, Skewness, and kurtosis.
Table 1: Descriptive Statistics for appropriate Variables
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Turnover (2012)
.22
28574.00
1244.52
5185.81
5.39
29.38
Number of Employees
.00
37187.00
3485.60
8183.81
3.10
10.24
Return on Capital Employed (2012)
-279.36
335.63
18.79
100.63
.41
5.25
Earnings per share (2012)
-6.50
221.00
25.43
53.91
2.8
7.92
Percentage Non UK Turnover
.06
51578.00
1866.82
9394.23
5.4
29.92
Number of Board Members
3.00
12.00
6.50
1.79
1.2
3.03
Percentage Non-Executive Director
1.00
9.00
3.43
1.86
1.3
2.11
CEO/ Chair Duality
.00
.00
.00
.00
.
.
Percentage of Female Executive Director
.00
3.00
.76
.77
.92
.92
Percentage of International Director
.00
4.00
.26
.78
4.06
18.51
Valid N (list wise) 30
Table 2: Correlation Analysis
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Return on Capital Employed (2012)
Earnings per share (2012)
Percentage Non UK Turnover
Number of Board Members
Percentage of Female Directors
Percentage of Non-Executive Directors
Percentage of International Directors
Return on Capital Employed (2012)
1
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Earnings per share (2012)
.255
1
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Percentage Non UK Turnover
.387*
.150
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Number of Board Members
.378*
.329
.483**
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Percentage of Female Directors
.315
.154
.165
.455*
1
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Percentage of Non-Executive Directors
.024
-.036
.273
.494**
.377*
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Percentage of International Directors
-.390*
-.132
-.053
.071
-.137
.446*
1
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*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Table 3: Regression Analysis for Firm Performance
Standardized Beta Coefficient
Independent Variable
Dependent Variable
Return on Capital Employed
Dependent Variable
Earnings Per Share
Dependent Variable Percentage International Turnover
Turnover (2012)
.299
.119
1.097**
Number of Employees
-.012
-.046
-.110**
Logsize
.176
.263
-.019*
Percentage of Non-Executive Directors
-.040
-.184
-.001
Percentage of Female Directors
.147
.091
.003
Percentage of International Directors
-.354
-.060
.017*
Adjusted R²
.185
-.130
.999
*= .05-level. **= .001-level.
References
Fama, Eugene, and Jensen, M. (1983) Separation of Ownership and Control.
Field, A., Miles, Jeremy, and Field, Zoe. (2012) Discovering Statistics using R. Sage Publications Ltd.
Jensen, Michael C. (1993) The Modern Industrial Revolution, Exit, and the Failure of Internal Control Systems. Blackwell Publishing for the American Finance Association.
Journal of Law Economics, 48(3), pp.831-880.
RGS. (2013) Sampling Techniques. [Online]. [Accessed on 23 June, 2013]. Available at: http://www.rgs.org/OurWork/Schools/Fieldwork+and+local+learning/Fieldwork+techniques/Sampling+techniques.htm