Descriptive statistics is a data analysis coefficient that presents information in a meaningful way. Statisticians use these tools to show, describe, and summarize patterns arising from specific data sets. Understanding raw data presents a significant challenge, especially if they are in large volumes. The simple interpretations that arise from descriptions give meaning to data and relate them to real-life situations. Descriptive statistics further focus on specific aspects of datasets like their central tendency and spread from the mean and mediums. It is easy to understand means and extremities when data is presented in a simplistic nature using real-life aspects. The analysis of the data set provides evidence that the principles of descriptive statistics including returns, board membership, the Q value, and assets are vital in understanding the financials of an organization.
The table illustrates the value of different variables relating to companies. The information is critical in evaluating the internal and external environment of organizations. The data set utilizes the tools of descriptive statistics, including standard deviation, maximum, minimum, and mean. There is also an incorporation of the observation values ranging from 759 to 824. The first variable in the table is the accrual detection of company earnings using the Modified Jones Model (1995). From the table, the Modified Jones model shows a mean earning management of 0.170 or 1.70%. The minimum earning management is 0, while the maximum is approximately 27.74%. Earning management refers to strategies that managers in organizations use to manipulate the revenue and returns to achieve their goals. The aim of smoothing earnings is to present a desirable picture to analysists and prospective investors. Zalata and Roberts (2017), state that the misclassification of essential earnings is a method of manipulating financial reports. The assessment of Egypt by the World Bank showed that it applies around 62% of corporate governance principles (Ebrahim & Fattah, 2015).
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From the table, the average manipulation of earnings is 1.71%, according to the Modified Jones model. The Kothari (2005) model presents higher averages for earning management. From the data, Kothari (2005) shows an average of 1.71%. The Kothari model (2005) tries to enhance prediction by adding an intercept and managing the effects of performance (Kothari, Leone & Wasley, 2005). The maximum in the data is, however, similar to that of the Modified Jones Model, 27.74%. Kothari also shows a higher minimum value of earning management at 0.0000179. Several studies have indicated that the Jones Model is more effective than Kothari’s (Dechow, Sloan & Sweeney, 1995). These differences in mean and minimum earning managements are nonetheless negligible due to their insignificant values.
From the data set, the average returns on assets (ROA) are 1.556. ROA is the measure indicating the profitability of a company. Firms that have effective utilization of assets to generate income will have a high ROA. The ROA can also be negative when companies make losses. The table shows -48.645 as the minimum returns on assets. The firms with the highest ROA have values of 33.515. The mean value of ROA, 1.556, shows little income to the assets. These figures offer essential information about the efficiency of a company and the industry. The source of the data above may, therefore, be from an inefficient firm or an industry with low income to the assets.
The table indicates 2.198 returns on equity (ROE). The minimum ROEs are -104.984 while the maximum is 135.614. The calculation of ROE involves dividing the net income by the equity of the shareholders. ROE in a firm represents the returns on net assets. It is also an illustration of the ability of a firm to create profits with its assets. While the ROA indicates the ratio of income generation, ROE narrows down to the profits that investors get. From the table, the average, minimum, and maximum values for ROE are higher than those of ROA. The returns on equity are always higher than those on assets since they represent the total assets after the removal of liabilities.
The figure shows that the mean value of Tobin’s Q is 1.02. The Q ratio shows the relationship between the market valuation of a company and its intrinsic value. When the Q value is low (0 to 1), the cost of replacing assets is higher than the total amount of the stock. Such figures show undervaluing of the company's stock. From the table, 1.02 indicates that the assets are slightly more valuable than the capital. Statisticians measure Tobin’s Q value by dividing the market value of the firm’s assets by its costs of replacement. When the value of Q is greater than 1, the costs of replacing assets are lower compared to their prices. High values of Q thus indicate the undervaluing of a firm's stock. The table shows that the minimum amount of Tobin's Q is 0.25 while the maximum is 8.07.
The mean gender diversity in the figure is 4.12% or 0.0412. Gender diversity refers to the ratio of women managers and directors to the total size of a company’s board. From the values in the data above, many companies have been unable to achieve gender equality. Men are still dominant in the top positions of organizations. According to past research, companies dealing with products like sanitary pads have more women in senior management positions such as CFO’s (Peni & Vähämaa, 2010). The maximum value of gender diversity is 0.6, while the minimum is 0. Zero implies that firms have no women as directors or members of their boards. Studies indicate that the number of women is also limited in areas such as auditing (Ittonen, Vähämaa & Vähämaa, 2013). Gender equality and women empowerment have been significant issues in most companies. Despite the legislative efforts of the government, females continue to face substantial barriers in most organizations. They have to face discrimination, such as lower job groups, less pay for equal work, and gender insensitive company policies.
The table above further shows a mean age diversity (AD) of 0.535. The calculation of age diversity takes the ratio of the director's age to the average age of the board members. From the data, there is high age diversity with an average of 53.5%. The minimum diversity is 0, while the maximum is 1. A value of 1 illustrates that the director is younger than 48 years. On the other hand, the minimum age diversity is 0. In such cases, the directors are aged 48 years old and above. Descriptive statistics that discuss age can utilize 0 or 1 as dummy figures. These form categories that are mutually exclusive and are more effective in describing the influence of some statistical variables. The average national diversity is 11.3%. National diversity is the ratio of foreign directors to the size of the board. The mean figure shows a limited number of top foreign officials in the company. The maximum national diversity from the table is 85.71% while the minimum is 0%.
The average board size from the data is 6. The board of directors in many companies, therefore, consists of 6 members. Board size has several effects on an organization. First, it determines the range of expertise that governs the company. More directors will mean a greater field of experience in different areas of management. The minimum number of board directors from the data is 2, while companies with 11 members on the board have the highest numbers. These numbers are lower compared to Susanto (2016), who indicates a maximum board membership of 15. The average independence of directors is 2.24% from the data in the table. 50% is the highest number of independent directors. In such cases, half of the board is independent and do not have any material relationship with the organization apart from their role of sitting at the board. The minimum value of independent directors is 0. Here all the directors have a connection to the company in addition to their position as board members.
The average rate of duality from the data is 41.5%. The data shows that nearly 50% of directors are also CEOs in their companies. The minimum value for duality is 0, while the maximum is 1. These are mutually exclusive dummy figures. Cases with directors as CEOs are represented by figure 1 while firms, where the director is not a CEO, are 0. The average number of firms owned by families is 41.75%. A family firm is an organization where at least one member of the founding family is on the board or holds a position of management (Ebrahim & Fattah, 2015). The variable can thus be represented by dummy figures where the maximum value is 1 while 0 is the minimum.
The average firm age in the dataset is 24.7 years. Firm age refers to the number of years that a company has been in operation. Many organizations present during the collection of data were, therefore, started around 25 years ago. From the data, the minimum number of years that a firm has been active are 2 years while the maximum years in operation is 63. The average age of companies offers essential information regarding the industry. The data may mean that firms take more than 20 years to attain stability. 20 years may also be the maximum age above which the rate of failure increases. Before 20 years, the closure rate of companies in the industries is high. The average value of assets that companies have is 178.43. Total assets of an organization represent its liabilities added to the equity of the shareholders. The maximum value of the total assets is 3709.937, while the minimum is 1.52. These values are vital since they illustrate what businesses own. An organization can further convert assets into cash to increase its liquidity.
The table also shows the leverage in the industry. To get leverage, economists divide the total debt that an organization by the total equity. The data set shows average leverage of 53.38, 762.923 as the maximum, and a minimum of 0. Leverage is a vital indicator of the financials of a company. The value shows the amount of debt that an organization uses to fund its activities. Investments using leverage are also critical in increasing the value of shareholders. The data also illustrates an average current ratio of 3.0, a minimum of 0, and 762.923 as the maximum value. Finding current rates involves the division of current assets by the liabilities. The current ratio in an organization indicates its liquidity or efficiency. It is thus critical since it shows the ability of a firm to meet its short-term liabilities using existing assets. From the data, many companies have a limited ability to pay off their short-term debts. In most cases, firms have less time to raise funds for such needs. It is, therefore, critical to have current assets like cash and other items readily marketable.
The sales growth is another critical variable when studying the finances of a company. The table above shows an average sales growth of 23.36. The maximum sales growth is 3290.867 while the minimum values are -397.3448. Sales growth represents the sale volume increases of a company from one year to another. The indicator is also critical when analyzing the performance of a firm. Sales growth shows the role of the marketing team in increasing the revenue of the company over a specified period. Higher sales growth further indicates the survival of a company and its financial growth path.
Cash flow refers to the amount of money coming in and out of an organization. The difference between the money present before a trading period and at the end of it can also be a method of measuring the cash flow. The data table shows an average cash flow of -1.482, minimum values of -217.56, and maximum cash flows of 103.35. The data also shows that the average dividends per share are 1.05%. Maximum bonuses are 20% while the minimum value is 0. From past research, the presence of board commissioners can reduce the challenges resulting from earnings management due to free cash flow (Susanto, Pradipta & Djashan, 2017). The cash flow calculation involves dividing the dividend by the actual number of shares. Firm loss is an indication of whether a company is making profits or losses. The table above shows an average loss of 26.7%. The maximum and minimum losses are dummy figures represented by 0 or 1. The value of 1 indicates that a company is making losses. On the other hand, 0 means that firms are accruing profits.
Descriptive statistics provide the opportunity for an in-depth understanding and evaluation of information. Large volumes of data can be tiresome and challenging to conceptualize. A detailed description will also compare data with past literature. The components of descriptive statistics vary depending on the kind of information. For an organization, standard variables that statisticians explore include the number of board members, ROA, ROE, cash flow, earnings management, gender diversity, and family ownership. Understanding these variables can offer insight into emerging trends like ethics, gender equality, and age discrimination. Conducting a descriptive statistics is thus an essential technique of relating data to real-life organizational issues.
References
Dechow, P., Sloan, R. G., & Sweeney, A.P. (1995). Detecting earnings management. The Accounting Review , 70, (2), 193-225.
Ebrahim, A., & Fattah, T. A. (2015). Corporate governance and initial compliance with IFRS in emerging markets: The case of income tax accounting in Egypt. Journal of International Accounting, Auditing and Taxation , 24 , 46-60.
Ittonen, K., Vähämaa, E., & Vähämaa, S. (2013). Female auditors and accruals quality. Accounting Horizons , 27 (2), 205-228.
Kothari, S.P., Leone, A.J. & Wasley, C.E. (2005). Performance matched discretionary accrual measures. Journal of Accounting and Economics , 39, 163-197.
Peni, E., & Vähämaa, S. (2010). Female executives and earnings management. Managerial Finance , 36 (7), 629-645.
Susanto, Y. K. (2016). The effect of audit committees and corporate governance on Earnings Management: Evidence from Indonesia manufacturing industry. International Journal of Business, Economics and Law , 10 (1), 32-37.
Susanto, Y. K., Pradipta, A., & Djashan, I. A. (2017). Free cash flow and earnings management: board of commissioner, board independence and audit quality. Corporate Ownership and Control , 14 (4-1), 284-288.
Zalata, A. M., & Roberts, C. (2017). Managing earnings using classification shifting: UK evidence. Journal of International Accounting, Auditing and Taxation , 29 , 52-65.
