Analysis Of Relationship Between Board Effectiveness And Voluntary Disclosure Of Carbon Essay.

The breakdown of the marks and task plan is as follows:

• Data Collection:

Accurate and complete

• Data Analysis:

Clearly presented, appropriate, descriptive statistics for the data.

• Proposed Data Analysis:

Appropriate inferential statistics identified to test your hypothesis.                                                                                                

Data is collected and correctly input into the spreadsheet

Insightful, clearly presented, data analysis

Inferential statistical analysis proposed that is appropriate for testing the hypotheses

The main aim of the current research is to examine the relationship between the effectiveness of the Board and the Voluntary Disclosure of the carbon causing the climate change. For the analysis purpose the secondary data has been collected for 60 firms in different countries in different industries. For selecting the data from the master file random sampling method was used so that the sample is not biased and the results from the analysis can be generalized to the entire population.

Once the final sample from the master data has been extracted, it was an important task to clean the data so that the robust results can be obtained. For the data processing the sample data was extracted to the a new excel sheet. The data set contains huge amount of information, so first only the required variables were retained and rest of the variables were removed from the final data. The variables which were retained include the voluntary disclosure score (dependent variable), highest body making the decision for voluntary disclosure (independent variable) and other control variable such as  the name of the country and the whether the disclosure has been made or not. After the variables were short listed the missing data part was addressed. There were missing data for the independent variable and the missing data were coded as -99 so that it can be easily identified. Once the data has been cleaned the data was imported to SPSS for further analysis.

Once the data was imported to SPSS the name of the variable was given as per the excel sheet. Similarly the scale of the variables was set accordingly. For example the disclosure score is the continuous variable so the scale measure was set for this variable. For the categorical variable it can be either the nominal variable or the ordinal variable. The only difference between the ordinal and the nominal measure is that in case of the ordinal there is proper order for the category such as the age group or the income group. However on the other hand there is no particular order for the nominal variable. In this case there were no ordinal variable, so all the categorical variable were set as the nominal. Once the data preparation in SPSS was completed then further analysis was conducted and the results from the analysis are discussed in the next section.

The descriptive statistics provide the overview of the data collected for the analysis purpose. In this case the descriptive statistics has been shown only for the dependent variable. This is because the numerical descriptive statistics are only appropriate for the scale variables. For categorical the graphical representation is more suitable.

Objective of the Study

Table 1 Descriptive statistics for the disclosure score of the firms

Results from the descriptive statistics for the disclosure score are shown in the table above. For the descriptive statistics various measures of the central tendencies has been shown. This includes mean, mode, median, maximum and minimum value, kurtosis and skewness.

The mean disclosure score is 90.38 with the standard deviation of 21.97. The standard deviation shows the variation in the data set. If the standard deviation is high than it can be said that the most of the data points are far from the series average value. On the other hand low standard deviation indicates high concentration of the values around the series mean. In this case the standard deviation is neither too high nor too low. Furthermore the results show that the minimum score is 0 whereas the maximum score is 100. This indicates that there are firms which have the lowest possible value and also the firms which have achieved the highest possible value. This indicates that all types of firms are included in the data set.

Figure 1 Descriptive statistics for the countries

One of the categorical variable included in the data set is the country the firms belong to. Results shows that most of the firms are based in USA followed by United Kingdom. Other contries includes France, Germany and Spain. This results indicates that the data was collected from the frrms which are based on developed countries. Since pollution has become one of the measure threat in the recent years, the developed countries have imposes serious restrictions on its firms.

Figure 2 Descriptive statistics for public disclosure of the firms included in the study

One of the variable included was whether the firms make the public disclosure or not. As shown in the figure above, results shows that 95 % of the firms disclose the score publicly.

Figure 3 Descriptive statistics for the highest level of direct responsibility

One of the important variable of interest in the current research is the highest level of direct responsibility for the disclosure. As shown in the figure above the highest body 91 % of the firms is board and only for the 9 % it is the senior manager. This indicates that the directly responsibility is of board which is the highest decision making body for any organization. This also shows how serious the problem of climate change has become.

Methodology

For the inferential analysis the chi square test and the correlation analysis has been conducted and the results from the each analysis has been discussed below.

Chi square test

The chi square test is used to examine whether there is any statistical difference between the mean values of the dependent variable for different categories.  So in this case at least one of the variable should be the categorical variable. In the current case, chi square has been conducted to examine whether there is statistically significant difference in the disclosure score for different countries. Results from the analysis are shown in the table below.

Table 2 Results from the chi square test

As shown in the above table the chi square value of 74.059 with 80 degrees of freedom is not statistically significant. This is because the p value is more than 0.05. So the null hypothesis cannot be rejected. In other words there is no statistically significant difference in the disclosure for firms in different countries. In other words, all the countries included in the study have similar disclosure score. This is may be because all the countries are developed and they have similar type of regulations for disclosure.

Table 3 Results from the chi square test

Furthermore the results from the symmetric measures also shows that the significance value are more than 0.05 so the null hypothesis can be rejected.

Correlation analysis

The correlation analysis is conducted to investigate how the two variable are related. The variable can be either positively related or negatively. If the variables moves in the same direction then it can be said that they are positively related whereas if they move in different direction, then the coefficient is negative. The value of the correlation coefficient lies between -1 and +1. Coefficients close to +1 indicated significant and positive correlation between the two variables. Whereas on the other hand correlation coefficient close to -1 indicates negative and strong correlation etween the two values.

For the current research also to examine the relationship between the disclosure score and the highest level of direct responsibility the correlation analysis was conducted and the results are shown in the table below.

Table 4 Results from correlation analysis

The correlation matrix suggests that the correlation coefficient between the dependent and the independent variable is 0.243. This indicates that there exists a positive relationship between the two variables. However the coefficient is not very strong, as the coefficient is not close to 1. This indicates that if the board member of the firms decide to disclose the score then the score is high, whereas sometimes the board’s decision to not publish the score negatively affect the disclosure score. Correlation coefficients of other variables are also shown in the table above and they can also be interpreted in similar way.