Descriptive Statistics

As explained in the previous chapter, three different models (i.e. Jones (JM), modified Jones (MJM), and performance-matched (PM) models) are applied in the present study to estimate discretionary accruals. While total accruals (TA) is used as the dependent variable, change in revenues (∆REV), change in receivables (∆REC), property, plant and equipment (PPT), and return on assets (ROA) are the independent variables. A cross-sectional approach is adopted to estimate discretionary accruals in the three models. This approach is based on year and type of industry classification to predict discretionary accruals. Therefore, the descriptive statistics for each variable included in the three models presented in the following tables are based on year and type of industry, scaled for the period from 2008 to 2010. Given that the variables are scaled by total assets at the beginning of the year, the values of these variables can be interpreted as percentages of total assets.

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5.2.1 Total Accruals (TA)

TA is used in the three aforementioned models as a dependent variable. Table 5.1 shows that the mean value of TA for the full sample during a given period is (-0.059), while the minimum and maximum values are (-0.814) and (0.494) respectively. Since TA is calculated as the difference between earnings before extraordinary and abnormal items (EBXA) and operating cash flow (OCF) divided by total assets (see Section 4.5.1.1 of Chapter 4), the results suggest that the mean value of EBXA is less than OCF by (-0.059). It can also be seen that the mean value of TA across years and industries is negative, suggesting that, on average, the EBXA are lower than OCF. Based on the year scaled, the highest average of TA is (-0.051) in 2010, while the lowest value is (-0.072) in 2009. These results indicate either that OCF in 2010 is less than OCF in 2009 or that the EBXA in 2010 is higher than its counterpart in 2009. Comparing the average of TA based on type of industry, Table 5.1 shows that the Technology industry has the highest average with (-0.040) compared with other industries, while the Media sector has the lowest average with (-0.115). In addition, Table 5.1 shows that the minimum value is (-0.814) in 2008 and the maximum is (0.494) in 2010, while the minimum and maximum values of TA are (-0.814) and (0.494) for the Media and Personal & Household Goods industries respectively.

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Table 5-1: Descriptive Statistics by Year and Industry Scaled for Total Accruals (TA)

Total Accruals (TA) Mean Min P50 Max Sd.

Full sample -0.059 -0.814 -0.052 0.494 0.081 Year 2008 -0.055 -0.814 -0.044 0.298 0.094 2009 -0.072 -0.394 -0.062 0.131 0.072 2010 -0.051 -0.278 -0.051 0.494 0.073 Industry

Oil & Gas -0.048 -0.272 -0.054 0.298 0.095

Industrial Goods & Services -0.059 -0.394 -0.048 0.093 0.063 Food & Beverage -0.045 -0.183 -0.041 0.042 0.051 Personal & Household Goods -0.056 -0.342 -0.040 0.494 0.138

Health Care -0.047 -0.134 -0.046 0.016 0.038

Retail -0.057 -0.278 -0.055 0.131 0.070

Media -0.115 -0.814 -0.076 0.074 0.159

Travel & Leisure -0.062 -0.213 -0.061 0.060 0.048 Telecommunications -0.102 -0.270 -0.108 0.167 0.093

Technology -0.040 -0.175 -0.043 0.103 0.060

Total accruals =[ Earnings before extraordinary and abnormal items (EBXA) - Operating cash flow (OCF)]/Total assets

5.2.2 Change in Revenue (∆REV)

∆REV is an independent variable used in the three models. Table 5.2 shows that the average value of ∆REV for the full sample and a given period is (0.077), indicating that the average value of ∆REV is increased by (0.077). The minimum and maximum values are (-1.763) and (0.877) respectively. Based on the year scaled, Table 5.2 reports that the highest value of ∆REV is (0.122) in the year 2008 and the lowest is (0.048) in 2009, suggesting that the average ∆REV is increased by (0.122) in 2008 and by (0.048) in 2009. Table 5.2 also shows that, on average, revenues increase by (0.061) in 2010. Comparing ∆REV across industries, the highest average is (0.112) in the Telecommunications sector and the lowest is (0.031) for Personal & Household Goods. Across years and industries, the minimum value is (-1.763) in the year 2008 for the Oil & Gas industry, while the maximum is (0.877) in the year 2009 for the Personal & Household Goods sector.

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Table 5-2: Descriptive Statistics by Year and Industry Scaled for Change in Revenue (∆REV)

Total Accruals (TA) Mean Min P50 Max Sd.

Full sample 0.077 -1.763 0.068 0.877 0.188 Year 2008 0.122 -1.763 0.105 0.733 0.225 2009 0.048 -0.757 0.056 0.877 0.195 2010 0.061 -0.351 0.044 0.692 0.125 Industry

Oil & Gas 0.052 -1.763 0.042 0.733 0.333

Industrial Goods & Services 0.072 -0.757 0.088 0.651 0.179 Food & Beverage 0.088 -0.117 0.070 0.368 0.103 Personal & Household Goods 0.031 -0.555 0.025 0.877 0.271

Health Care 0.091 0.001 0.095 0.340 0.072

Retail 0.096 -0.523 0.080 0.600 0.159

Media 0.041 -0.187 0.043 0.387 0.130

Travel & Leisure 0.098 -0.242 0.041 0.764 0.177 Telecommunications 0.112 -0.036 0.032 0.613 0.171

Technology 0.086 -0.351 0.099 0.309 0.126

Change in revenue (∆REV)=[ ]/Total assets

5.2.3 Change in Receivables (∆RCE)

∆RCE is deducted from ∆REV, (∆REV-∆RCE), when applying MJM and PM models to predict non-discretionary accruals. As can be noted from Table 5.3, the average value of ∆RCE for a given period and industry is almost positive, except for the year 2009. The average for the full sample is (0.012), indicating that, on average, ∆RCE is increased by (0.012) during the period. The minimum and maximum values are (- 0.263) and (0.846) respectively. Table 5.3 also shows that the highest average of ∆RCE is (0.027) in the year 2008 and the lowest is (-0.004) in 2009. These findings indicate that the average ∆RCE in 2008 is higher than its counterpart value in 2007 and has increased by (0.027), while in 2009 it is lower than its counterpart in 2008 and has decreased by approximately (-0.004). By comparing the lowest and the highest values of ∆RCE based on industry, it can be seen that the lowest average value is (0.002) for the Telecommunications sector, while the highest is (0.026) for

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the Retail industry. This indicates that the average ∆RCE in Telecommunications is lower than the average ∆RCE in the Retail industry.

Table 5-3: Descriptive Statistics by Year and Industry Scaled for Change in Receivables (∆REC)

Total Accruals (TA) Mean Min P50 Max Sd.

Full sample 0.012 -0.263 0.005 0.846 0.058 Year 2008 0.027 -0.136 0.019 0.312 0.047 2009 -0.004 -0.263 -0.005 0.846 0.081 2010 0.012 -0.092 0.005 0.142 0.031 Industry

Oil & Gas 0.017 -0.136 0.012 0.134 0.048

Industrial Goods & Services 0.007 -0.224 0.011 0.120 0.049 Food & Beverage 0.007 -0.092 0.009 0.060 0.027 Personal & Household Goods 0.007 -0.063 0.001 0.183 0.038

Health Care 0.018 -0.025 0.019 0.068 0.024

Retail 0.026 -0.263 0.005 0.846 0.114

Media 0.005 -0.094 0.003 0.142 0.047

Travel & Leisure 0.005 -0.049 0.001 0.274 0.039 Telecommunications 0.002 -0.028 0.003 0.039 0.018

Technology 0.021 -0.063 0.021 0.113 0.039

Change in revenue (∆REC)=[ ]/Total assets

5.2.4 Gross Property, Plant and Equipment (PPT)

PPT is an independent variable included in the three models to control for the effect of depreciation, depletion and amortisation (Jones 1991). In general, the average value of PPT for the full sample is (0.467) during the period from 2008 to 2010. The minimum value is (0.004), while the maximum is (0.737), indicating that the range of PPT is between (0.004) and (0.737) of total assets at the beginning of the year. Based on the year scaled, Table 5.4 shows that the highest average of PPT is (0.494) and the lowest is (0.451) in the years 2009 and 2008 respectively. It ranges between (0.004) and (0.737) during the period 2008-2010. Based on the industry scaled, the highest value is (0.949) in the Telecommunications sector, while the lowest is (0.057) for Personal & Household Goods.

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Table 5-4: Descriptive Statistics by Year and Industry Scaled for Gross Property, Plant and Equipment (PPT)

Total Accruals (TA) Mean Min P50 Max Sd.

Full sample 0.467 0.004 0.346 0.737 0.403 Year 2008 0.451 0.004 0.315 0.469 0.390 2009 0.494 0.004 0.366 0.737 0.449 2010 0.455 0.004 0.355 0.456 0.367 Industry

Oil & Gas 0.592 0.060 0.675 0.307 0.339

Industrial Goods & Services 0.455 0.086 0.299 0.403 0.338 Food & Beverage 0.567 0.183 0.481 0.568 0.342 Personal & Household Goods 0.057 0.004 0.028 0.240 0.067

Health Care 0.258 0.048 0.263 0.518 0.171

Retail 0.641 0.138 0.587 0.456 0.327

Media 0.134 0.025 0.093 0.422 0.127

Travel & Leisure 0.673 0.088 0.700 0.433 0.360 Telecommunications 0.949 0.047 0.527 0.737 0.945

Technology 0.202 0.020 0.103 0.134 0.258

5.2.5 Return on Assets (ROA)

ROA is used as an independent variable in the PM model to control for the impact of the firm’s financial performance. Table 5.5 shows that the average ROA for the full sample is (0.083) and ranges from (-0.544) to (0.751). It also shows that the highest average of ROA is (0.089) during 2008 and the lowest is (0.077) in 2009, suggesting that the firms’ highest financial performance is in 2008 compared with the other years. The minimum and maximum values range from (-0.544) to (0.751) during a given period. Based on the industry scaled, the lowest value is (0.044) in Personal & Household Goods and the highest is (0.109) in the Technology sector. The minimum value of ROA is (-0.554) in the Media industry and the maximum value is (0.751) in Industrial Goods & Services.

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Table 5-5: Descriptive Statistics by Year and Industry Scaled for Return on Assets (ROA)

Total Accruals (TA) Mean Min P50 Max Sd.

Full sample 0.083 -0.544 0.074 0.751 0.098 Year 2008 0.089 -0.544 0.083 0.658 0.117 2009 0.077 -0.105 0.065 0.751 0.105 2010 0.083 -0.165 0.071 0.374 0.067 Industry

Oil & Gas 0.076 -0.165 0.072 0.462 0.109

Industrial Goods & Services 0.084 -0.131 0.077 0.751 0.085 Food & Beverage 0.073 -0.067 0.075 0.197 0.050 Personal & Household Goods 0.044 -0.248 0.051 0.248 0.101

Health Care 0.087 -0.063 0.087 0.158 0.053

Retail 0.086 -0.172 0.084 0.556 0.090

Media 0.088 -0.544 0.047 0.658 0.216

Travel & Leisure 0.092 -0.015 0.067 0.473 0.097

Telecommunications 0.076 -0.156 0.070 0.184 0.085

Technology 0.109 -0.012 0.092 0.310 0.081

Return on assets (ROA)= Net income / total assets