5.8 Models specification and variable measurement
5.8.1 Objective 1- To ascertain the prevalence of RAM
The prevalence of RAM as an earnings management tool was not well understood until about a decade ago. Graham et al.’s (2005) survey involving more than 400 executives document the widespread use of RAM; 80% of the CFO in their study stated that they would decrease expenditure on R&D, maintenance, and advertising, to meet an earnings target, and 55% of the participants were of the view that they would postpone a new project, even if such delay caused a small loss in firm value. In line with this survey, Roychowdhury (2006) documents large-sample evidence which suggest that managers avoid reporting annual losses or missing analyst forecasts by using sales manipulations, reducing discretionary expenditures, and manipulating production cost. All of these
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activities are deviations from optimal operational decisions in order to bias earnings upward.
Identification of manipulation through real activities requires the empirical application of models. These models estimate the “normal” level of operational activities and, as such, their regression residuals represent the “abnormal” level, that is, they are proxies for management variables. In other words, the abnormal component of real activities is the difference between the actual observed value and the estimate obtained by applying the models (Gunny, 2005; Roychowdhury, 2006).
Following previous studies on RAM (Roychowdhury, 2006; Sanjaya & Saragih, 2012;
Sun et al., 2014), this study examines the following manipulation of real activities: sales manipulation (through reduction of cash flow from operation), discretionary expenses manipulation (by reducing discretionary expenditures), and production cost manipulation (through overproduction).
5.8.1.1 Sales manipulation
Computing abnormal operating cash flow derives sales manipulation. The manipulation could include offering more discounts, providing lenient credit terms as well as channel stuffing. Normal cash flow is estimated by regressing sales and change in sales on the annual operating cash flow using this model:
𝐶𝐹𝑂𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1 = ∝ (1 𝑇𝐴⁄ 𝑖,𝑡−1) + 𝛽1(𝑆𝐿𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1) + 𝛽2(∆𝑆𝐿𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1) + 𝜀𝑖,𝑡 ... (1)
Where:
𝐶𝐹𝑂𝑖,𝑡 = cash-flows from operations for firm i at period t.
𝑇𝐴𝑖,𝑡−1= the total assets for firm i at the end of the prior year.
∝ (1 𝑇𝐴⁄ 𝑖,𝑡−1) = Scaled intercept
𝑆𝐿𝑖,𝑡 = sales for firm i at year t (Current year sales).
∆𝑆𝐿𝑖,𝑡 = the change in sales of firm i in year t. It is measured by sales in year t minus sales in year t-1 for firm i.
All the variables, including the intercept (constant term), are scaled by total assets for firm i at the beginning of year t (𝑇𝐴𝑖,𝑡−1). To cater for the variations in the size of the firms to circumvent erroneous and unreliable correlation among the variables is the
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reason for scaling by lagged total assets; this prevents the results from being misleading (Gunny, 2005; Roychowdhury, 2006).
The change in sales reveals the overall expansion of a firm's business operation. Firms with growth potential are more likely to have a more substantial change in sales, while declining firms will exhibit a lower change in sales.
After the estimation of parameters in equation (1), abnormal cash flow (ab_cfoi,t) is the residual value of equation (1). The actual cashflow minus the 'normal' cashflow computed using the estimated coefficients from the corresponding industry-year model and the firm year’s sales and lagged assets gives abnormal cashflow for every firm-year.
More so, the signed value of abnormal cash flows from operations reduces with sales manipulation; a positive figure for ab_cfoi,t indicates low RAM while a negative residual indicates a high RAM (Roychowdhury, 2006).
5.8.1.2 Discretionary expenses manipulation
Discretionary expenses manipulation increases earnings because of the delay in recording these expenditures as expected of period costs. Companies that opportunistically cut discretionary expenses will have unusually lower discretionary expenses. Following Roychowdhury (2006), discretionary expenditure is the sum of R&D expenses, expenses from advertisements, and selling, general and administrative expenditures. The following model will determine the abnormal discretionary expenditures:
𝐷𝐼𝑆𝑋𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1 = ∝ (1 𝑇𝐴⁄ 𝑖,𝑡−1) + 𝛽1(𝑆𝐿𝑖,𝑡−1⁄𝑇𝐴𝑖,𝑡−1) + 𝜀𝑖,𝑡 …...… (2) Where:
𝐷𝐼𝑆𝑋𝑖,𝑡 = discretionary expenses of firm i in year t (current year discretionary expenses).
𝑆𝐿𝑖,𝑡−1 = lagged sales (sales in the prior year).
The second measurement of RAM is discretionary expenses manipulation (ab_disxi,t), which is the residual value of equation (2). Reducing discretionary expenditures leads to lower figures for abnormal discretionary expenses (ab_disxi,t), while a high figure reflects low RAM.
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Production cost manipulation involves producing more units of goods than required to boost earnings since the cost of goods sold decreases with such activities. When overproduction occurs, there is a lower cost of goods sold (COGS) since it is reasonable to spread the fixed cost over the entire overproduction. The level of production costs will abnormally increase if managers manipulate earnings by overproduction. Similar to Roychowdhury (2006), the production cost is the summation of the cost of goods sold and the change in inventory. The regression model to compute abnormal production costs (ab_prodit) is estimated, thus:
𝑃𝑅𝑂𝐷𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1 = ∝ (1 𝑇𝐴⁄ 𝑖,𝑡−1) + 𝛽1(𝑆𝐿𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1) + 𝛽2(∆𝑆𝐿𝑖,𝑡⁄𝑇𝐴𝑖,𝑡−1) + 𝛽3(∆𝑆𝐿𝑖,𝑡−1⁄𝑇𝐴𝑖,𝑡−1) + 𝜀𝑖,𝑡 …... (3)
Where:
𝑃𝑅𝑂𝐷𝑖,𝑡 = production costs of firm i at year t.
∆𝑆𝐿𝑖,𝑡−1 = lagged change in sales, is sales in year t-1 minus sales in year t-2 for firm i (∆𝑆𝐿𝑖,𝑡−1 = ∆𝑆𝐿𝑖,𝑡−1− ∆𝑆𝐿𝑖,𝑡−2)
The third measure of RAM is abnormal levels of production cost. The unstandardized residuals value, that is, abnormal production (ab_prodit) in equation (3) is assumed to be the magnitude of RAM practices. The perception is that higher residuals result in higher overproduction to decrease COGS and inflate reported earnings. The residual is the deviation in production cost not explained by the variables.
The functioning of this formation eases the application to any industry, be it manufacturing or otherwise (Cupertino et al., 2016). In the same vein, Roychowdhury (2006) and Dechow, Hutton, Kim, and Sloan (2012) explain that the inclusion of the scaled intercept (1 𝑇𝐴⁄ 𝑖,𝑡−1) by total assets allows the independent variable to be different from zero even when there are no sales for the period "t" or "t – 1". Gunny (2010) is of the view that analysis according to production costs – rather than by the COGS or changes in inventory – is an important consideration to avoid the confounding influence of accruals-based management. Also, a manager's decision to delay writing off a stock of obsolete products to reduce the cost of products sold is an abnormally low COGS. As a result, if COGS become the variable of analysis, the effects of accrual- based management could be erroneously classified as the effects of RAM. In contrast,
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by using production costs, that is, COGS and difference in inventory – the impact of accruals would not be confused with that of real activities because there will be a compensating effect between the reduction in COGS and the difference in inventory (Cupertino et al., 2016).
5.8.2 Objective 2: To examine the impact of audit committee attributes on RAM