Analytical Procedures

This section presents the statistical methods used to analyse the data. All statistics are processed on the STATA 12 statistical software. Data analysis presented in the next two chapters is structured as follows.

First the author describes the data and checks its normality in the descriptive statistics section. The mean, standard deviation, median, minimum, maximum, skewness and kurtosis are the statistics used to describe the data. The skewness and kurtosis determine the nature of the data whether normally distributed (parametric) or not (non-parametric) (Hair 2010). As a rule of thumb the data is normally distributed if its standard skewness and standard kurtosis lie within ±1.96 and ±3 respectively (Gujarati 1995). In case the data is non-parametric, winsorizing or transformation methods are applied to enhance the normality of the distribution.

Then the correlations among variables are reported in the correlation matrix section. Correlation coefficients are used to gauge the strength of linear association between two variables (Gujarati 2003). Both of the Spearman and Pearson correlations are employed, however, the main relevant analysis is based on Spearman if the data is non-parametric and on Pearson if the data is parametric. Correlation coefficients are then used as indicators for multicollinearity problems. As a rule of thumb, if two variables have a correlation coefficient greater than 0.80 then they are considered to be highly collinear (Gujarati 2003).

A multivariate analysis section is after that presented as the multivariate analysis accounts for the variation in firms’ characteristics and the other determinants of the dependent variables which are not controlled for under the correlation matrix. Before running the regressions, the validity of applying Ordinary Least Square (OLS) regression, the most commonly used method in the literature, is

181

tested. There are five basic assumptions to be satisfied in order for the OLS estimator not to be biased (Gujarati 2003). These assumptions are:

- The error term is normally distributed.

- The error term is homoskedastic i.e., it has constant variance - The error term is not serially correlated.

- The repressors are not highly correlated. - The model is linear.

The empirical models tested in this thesis are expected to suffer from heteroscedasticity problems. These problems might arise as a result of the presence of outliers especially in the earnings management and auditor remuneration variables. Another source of heteroscedasticity might be the skewness in the distribution of one or more independent variables in the models (e.g., ACM, Manown, Growth, and DUAL). To check for heteroskedasticity, either the Breusch-Pagan/Cook-Weisberg or White’s tests for heteroskedasticity is used when the employed regression is pooled OLS. The Modified Wald test, however, is used for group-wise heteroskedasticity in fixed effect regression models. Moreover, autocorrelation problems might arise in case there are omitted variables. Therefore, regression standard errors are adjusted for heteroskedasticity and autocorrelation through the use of robust standard errors and robust standard errors clustered by firm.

In addition to the correlation coefficients, checking for multicollinearity among repressors is done also through the Variance Inflation Factor (VIF) and tolerance value statistics. As a rule of thumb, variables with VIF value in excess of 10 or tolerance value of less than 0.10 are regarded as highly collinear (Gujarati 2003, p.362). Simple and panel effects regressions are used. Where appropriate, the Breusch-Pagan Lagrange Multiplier (LM) test is conducted to help decide on

182

whether to use a random-effect regression or simple OLS one. The Hausman test is then used to help decide between random and fixed effects regressions.

Similar to other comparative studies in corporate governance, the results of this thesis may suffer from endogeneity problems. The author uses several ways to mitigate such problems including incorporating additional control variables, using fixed effect regression and incorporating residuals as regressors in jointly- determined models.

Finally additional tests to check the robustness of the results are presented in the additional analysis section. Extant research finds that the performance of governance mechanisms may differ between recession periods and regular periods. As such the author includes descriptive and multivariate analysis to test the hypotheses in the pre-financial crisis period from 2005 to 2007. Other tests for additional control variables and different variable definitions are also provided.

5.6 Summary

The author examines the impact of corporate governance on financial reporting quality and auditor remuneration through the use of two empirical models. This chapter presents the hypotheses development and research design of each of these empirical models. Specifically, research hypotheses are developed through the use of theoretical links between the hypotheses variables supported by relevant empirical evidence. The specifications of the models under study are then presented after discussing the measurements of each of the dependent variables and the control variables.

Data for the variables under study are collected from two sources: corporate governance data are collected manually from annual reports, whilst financial data are

183

obtained from DataStream. The initial sample consists of all FTSE 350 firms listed in the LSE for the three-year period between 2008 and 2010. After excluding firms in the financial, insurance and utilities industries as well as those with missing data, the final size of each of the financial reporting quality and auditor remuneration samples comprises 662 and 619 firm-year observations respectively. OLS regressions (either simple or panel) with robust standard errors and robust standard errors clustered by firm are used to test the hypotheses. The results are presented in the next two chapters.

184

Chapter 6. Findings and Discussion