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...earnings developed Hicks (1939). Hicksian income corresponds to the amount that can be consumed (that is, paid out as dividends) during period, while leaving the firm equally well off at the beginning and the end of the period (Hicks 1939, 176). This measure of income corresponds to the change in net economic assets other than from transactions with owners.
We focus on decision usefulness for two reasons. First, the FASB's Conceptual Framework states that the purpose of financial reporting is to provide information that is useful for business decisions (Concepts Statement No. 1, FASB 1978, para. 34 and following), and considers decision usefulness the overriding criterion for judging accounting choices (Concepts Statement No.2, FASB 1980, paras. 30 and 32). Decision usefulness thus presumably captures the intent of financial reporting standards. The FASB's shift to a focus on decision usefulness, and away from the long-standing focus on the stewardship function of accounting and the relation of reported earnings to economic eamings constructs, was driven by concerns about operationality (e.g., Beaver 1998). Economic income constructs, of which Hicksian income is an example, cannot be used to achieve consensus on financial reporting standards. Second, and equally important, decision usefulness is empirically tractable and commonly used in accounting research.
Because of its context-specificity, assessments of earnings quality from the perspective of decision usefulness inevitably confront a myriad of users and uses: the FASB's Concepts Statement No. 1, paras. 24-30, discusses over a dozen users and uses of financial reports. As a result, the decision usefulness of accounting earnings is evaluated in the context of assumptions about both the user and the use of the earnings number, and the conclusions are conditional on the context chosen. We complement the context-specific, empirically tractable decision usefulness perspective on earnings quality with the context-neutral but empirically non-operational perspective of representational faithfulness to Hicksian income.
We define earnings quality as the extent to which reported earnings faithfully represent Hicksian income, where representational faithfulness means correspondence or agreement between a measure or description and the phenomenon that it purports to represent" (FASB Concepts Statement No. 2, para. 63). We focus on Hicksian income because it abstracts from user-decision contexts; from accounting recognition rules, which preclude the recording of many economic assets and liabilities; from difficulties in reliably measuring assets and liabilities at their economic values; from the effects of management's judgments and estimates; and from the influence of auditors. The construct thus allows us to consider what reported earnings would look like in the absence of financial reporting rules and their implementation.
As a practical matter, reported earnings will not measure Hicksian income due to recognition and measurement rules in U.S. GAAP, combined with preparers' implementation decisions. Because Hicksian income is not observable, it is not possible to quantify the differences. However, there are better and worse approximations, and we argue that higher quality earnings are closer to Hicksian income. We use the construct to provide a neutral and context-free benchmark, in contrast to the decision usefulness construct, which is empirically tractable but heavily context-dependent.
In the remainder of this commentary we describe several earnings quality constructs and measures that have been used in academic accounting research and in teaching. (1) We discuss conceptual bases of these constructs; whether they are measurable and, if so, how; assumptions behind the measures; commonalities and differences across definitions; and the extent to which various empirical measures of earnings quality overlap or are mutually inconsistent. (2) We describe approaches used to measure earnings quality, some of the trade-offs inherent in choosing among the approaches, and some of the design choices inherent in empirical research related to earnings quality.
The rest of this commentary proceeds as follows. The next section describes why and to whom the quality of earnings is important. The third section discusses several approaches to defining earnings quality and links these definitions to empirical measurements. The fourth section addresses practical considerations in the estimation and use of earnings quality measures. The final section summarizes and concludes.
WHY (AND TO WHOM) IS EARNINGS QUALITY OF INTEREST?
Consistent with the focus on decision usefulness adopted by the FASB and by academic researchers, we believe that earnings quality and, more generally, financial reporting quality are of interest to those who use financial reports for contracting purposes and for investment decision making. In addition, we believe that standard setters view the quality of financial reports as an indirect indicator of the quality of financial reporting standards.
Earnings, and metrics derived from it, are commonly used in compensation arrangements and in debt agreements. Contracting decisions based on low-quality or defective earnings will induce unintended wealth transfers. For example, overstated earnings, used as an indicator of managers' performance, will result in overcompensation to managers. Similarly, overstated earnings might mask deteriorating solvency, leading lenders mistakenly to continue lending or to defer foreclosure.
From an investment perspective, low-quality earnings are undesirable because they provide a defective resource allocation signal. Low-quality earnings are inefficient because they reduce economic growth by causing capital to be misallocated. In the limit, earnings of such low quality as to be fraudulent are objectionable because they divert resources from substantive projects with actual expected payoffs to chimerical projects with imaginary expected payoffs.
Finally, when accounting standard setters seek feedback on whether the standards they promulgate are effective, they tend to focus on outputs, including reported earnings. The FASB's Conceptual Framework points to decision usefulness as the benchmark for assessing effectiveness; we also consider the extent to which the reported accounting income faithfully represents Hicksian income.
EARNINGS QUALITY CONSTRUCTS AND MEASURES
This section discusses several classes of earnings quality constructs that have been developed largely under the decision usefulness rubric, the ways the constructs are measured and used in accounting research, and the relation between the constructs and the idea that high-quality earnings faithfully represent Hicksian income, within the limits imposed by accounting standards. We distinguish earnings quality constructs that depend on both accounting treatments and underlying events and transactions (e.g., the economics of some business models significantly reduce the predictive ability of earnings) from those that depend primarily or entirely on accounting treatments (e.g., both smoothing and discretionary or abnormal accruals are reporting phenomena).
We consider earnings quality constructs derived from (1) the time-series properties of earnings; (2) selected qualitative characteristics in the FASB's Conceptual Framework; (3) the relations among income, cash, and accruals; and (4) implementation decisions. We do not view these four classes of earnings quality constructs as exhaustive-for example, we do not consider the valuation-oriented perspective adopted in Penman (200 l)-or as mutually exclusive-for example, earnings quality constructs derived from implementation decisions are sometimes measured in terms of the relations among income, cash and accruals.
Earnings Quality Constructs Derived from Time-Series Properties of Earnings
Time-series constructs associated with earnings quality include persistence, predictive ability, and variability. These three constructs are linked by the properties of the earnings innovation series; persistence captures the extent to which a given innovation remains in future realizations; predictive ability is a function of the distribution (especially the variance) of the innovation series; and variability measures the time-series variance of innovations directly.
Persistence
This construct is sometimes discussed in the context of sustainable or...
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