6.2 Descriptive statistics consideration
6.2.1 Descriptive statistics review result
There are several descriptive measures used in statistics, but for this research, only three measures, namely, mean, standard deviation and minimum/maximum, are presented, interpreted and discussed. Table 6.1 provides the descriptive statistics for the variables used in the models analysing the effect of audit committee attributes on RAM. Panel A shows the descriptive statistics for all the variables, while Panel B reports the frequency distribution of the dichotomous and discreet variables. The variables in Panel B includes audit committee independence (ACIND), audit committee financial literacy, (ACFLT), audit committee meeting frequency, (ACMTG), audit committee multiple directorships, (ACMDR), audit committee female directorship, (ACFDR), audit committee size, (ACSZE), audit quality, (ADQLT), introduction of corporate governance code 2003, (ICCGV), and review of corporate governance code 2011, (CCCGV). The frequency distributions give the analysis a more in-depth understanding.
Table 6.1: Descriptive statistics of the variables: Panel A All variables
Variable Observations Mean Standard
Deviation Minimum Maximum
RAM 1036 0.026 0.460 -3.676 1.518
AB_CFO 1036 -0.006 0.166 -0.616 0.490
AB_DISX 1036 -0.005 0.151 -0.330 0.659
AB_PROD 1036 0.015 0.293 -3.148 1.321
ACIND 1036 0.469 0.499 0 1
ACFLT 1036 0.812 0.391 0 1
ACMTG 1036 3.249 0.813 1 7
ACMDR 1036 0.896 0.786 0 5
ACFDR 1036 0.451 0.611 0 3
ACSZE 1036 5.291 0.977 2 7
ICCGV 1036 0.857 0.350 0 1
CCCGV 1036 0.286 0.452 0 1
BDIND 1036 0.765 0.136 0.2 1
ADQLT 1036 0.645 0.479 0 1
FRMSZ 1036 22.164 1.933 17.597 27.615
FRMGW 1036 1.364 1.398 -9.156 7.763
FRMLV 1036 0.765 1.074 0.025 16.573
139 Table 6.1: (continued) Descriptive statistics of the variables: Panel B Panel B: Frequency distribution of dummy and discreet variables
ACIND % ACFLT % ACMTG % ACMDR % ACFDR % ACSZE % ICCGV % CCCGV % ADQLT %
0 550 53.1 195 18.8 0 0.0 330 31.9 630 60.8 0 0.0 148 14.3 740 71.4 368 35.5 1 486 46.9 841 81.2 5 0.5 523 50.5 349 33.7 0 0.0 888 85.7 296 28.6 668 64.5
2 164 15.8 152 14.7 53 5.1 6 0.6
3 494 47.7 25 2.4 4 0.4 9 0.9
4 320 30.9 4 0.4 323 31.2
5 48 4.6 2 0.2 39 3.8
6 4 0.4 658 63.5
7 1 0.1 1 0.1
1036 100 1036 100 1036 100 1036 100 1036 100 1036 100 1036 100 1036 100 1036 100
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6.2.1.1 Audit committee variables involved in the descriptive statistics
The audit committee variables are independence (ACIND) measured by a dichotomous variable to represent non-involvement of executive directors in the committee membership as follows: if at least one member is an executive director “0” and “1” if otherwise; a dichotomous variable measures financial literacy (ACFLT): if at least one member is an expert in accounting/finance “1” and ‘0’ if otherwise; meeting frequency (ACMTG) measures the number of meetings held by the committee in a year; multiple directorships (ACMDR) measures the number of director seats occupied on other company board or audit committee of other firms; female directorship (ACFDR) measures the total number of female directors on the committee, and the entire membership of the committee measures size (ACSZE), that is, the composition of members in the committee.
6.2.1.2 The audit committee attributes’ descriptive statistics
This section discusses the descriptive statistics of all the audit committee variables mentioned above. The audit committee independence (ACIND) is measured by a dichotomous variable which takes “0” if at least one member is an executive director and “1” if otherwise. In Panel A of Table 6.1, the mean value of ACIND is 0.47. Panel B gives a clearer picture as it reveals that 46.9% of the firms involved in this study have the audit committee members entirely composed of non-executive directors during the years under review. For the proportion of audit committee members with financial literacy (ACFLT), also measured by a dichotomous variable, with an average of 0.81, Panel B confirms this by showing that 81.2% of the sampled firms have in the audit committee, financially literate members.
The audit committees’ meeting frequency (ACMTG) is between one and seven during the year, with an average of approximately three meetings per year, Panel B makes it more precise when it reveals that audit committees that met three times are 47.7% while 30.9% met four times. The two extremes are both less than one per cent, indicating that one meeting has 0.5%, and seven meetings have 0.1%. The multiple directorships of audit committee members (ACMDR) range between zero and five with an average of 0.90; Panel B reveals that audit committee members with no directorship seat in other firms are 31.9% while those with one directorship seat in other firms have the most significant percentage of about 50.5%. It implies that between two and five multiple
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directorships account for 17.6%, out of this, those with two directorship seats are about 14.7% while the extreme, which is five seats, has just 0.2%.
Female membership or directorship (ACFDR) has a range between zero and three with an average of 0.45 (Panel A). Panel B shows that, the audit committees with no female membership account for a high percentage of 60.8% while those with at least one female member are 33.7%. The upper range of the extremes, which is three female directorships accounts for a mere 0.4%. This statistic is an indication of the seriousness of the gender disparity in the audit committee of firms listed on the NSE. The sizes of the audit committees (ACSZE) ranged between two and seven members with an average of approximately five members. In Panel B, 98.5% (31.2% + 3.8 + 63.5%) of the firms have an audit committee whose members are between four and six. The two extremes, two and seven membership size, are 0.6% and 0.1% respectively.
6.2.1.3 Corporate governance codes’ variables involved in the descriptive statistics The variables of the corporate governance codes are a dichotomous variable which measures introduction of the SEC code in 2003 (ICCGV): coded ‘1’ for period after the introduction (2003-2010) and ‘0’ for period before the introduction; and measures the review of the SEC code in 2011 (CCCGV) by a dichotomous variable: coded ‘1’ for period after the change (2011-2014) and ‘0’ for period before the change.
6.2.1.4 Corporate governance codes descriptive statistics discussions
The introduction of the SEC code in 2003 (ICCGV) reveals a mean value of 0.86 and a standard deviation of 0.35 in Panel A, and it corroborates Panel B, which indicates 85.7% for the period after the introduction. There are more observations for the period after the introduction than for the period before the introduction as observed. The contrary position is in the descriptive statistics of the review of the SEC code in 2011 (CCCGV). Panel A has a mean value of 0.29 and a standard deviation of 0.45, while Panel B confirms this result, indicating that 28.6% of the observations fall within the period after the review of the SEC code in 2011 and that 71.4% is for the period before the change.
6.2.1.5 Control variables descriptive statistics discussions
This section discusses the descriptive statistics of the control variables. Audit quality (ADQLT), is measured by a dichotomous variable with a mean of 0.65 and standard deviation of 0.48; Panel B reveals that 64.5% of the sampled firms are audited by the
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‘Big 4’ audit firms while “non-Big 4” firms audit the remaining 35.5 %. Board independence (BDIND), represents the proportion of non-executive director on the board. It has a mean value of 0.765, and a standard deviation of 0.136. This statistic implies that 76.5% of the sampled firms’ boards of directors are composed of more non- executive directors than the executive directors. This confirms the independence of the boards of directors of most of the sampled firms. Firm size (FRMSZ) has a mean value of 22.16, and it indicates that the average total assets of most of the firms are in the range of twenty-two billion Nairas. It shows that the size of the sampled firms is significant. The standard deviation was 1.93. A large firm may engage in income- increasing earnings manipulation to mitigate political pressure (Soliman, 2019; Watts &
Zimmerman, 1990).
The firm growth (FRMGW) has a mean value of 1.36, and a standard deviation of 1.40.
The positive value of the mean is an indication that most of the sampled firms have a higher market value compared to their book value, an indication of growth. Firm leverage (FRMLV), which captures the likelihood of debt covenant violation by management in the process of manipulating its real activities, has a mean value of 0.77 and a standard deviation of 1.07. This statistic is an indication of a higher leverage ratio of most of the sampled firms. In the view of Smith et al. (2006), the higher the leverage ratio, the greater the risk of breaching some debt covenants. Baxter (2007), in the same vein, revealed that firms with high leverage are exposed to the risk of debt covenant constraints and are therefore exposed to a higher propensity to manipulate earnings. A high leverage position is not a good situation for any firm as it could also lead to a higher cost of debt financing (Piot & Janin, 2007).