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Understanding earnings quality - MIT Sloan School of Management

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ownership, strong investor rights, and strong legal enforcement. The Leuz et al. (2003) artificial<br />

smoothness measures are commonly used in cross-country studies as a proxy for <strong>earnings</strong><br />

management. The first proxy, measured at the country-level, is the median <strong>of</strong> the firm-level standard<br />

deviation <strong>of</strong> operating <strong>earnings</strong> divided by the firm-level standard deviation <strong>of</strong> cash flow from<br />

operations. The second proxy is the contemporaneous correlation between changes in accounting<br />

accruals and changes in operating cash flows. Both measures emphasize that smoothness represents<br />

<strong>earnings</strong> management when it is measured relative to inherent or fundamental smoothness <strong>of</strong> the<br />

firm’s operations. Operating cash flow smoothness is used to control for inherent smoothness.<br />

Lang, Raedy, and Wilson (2006) compare the extent <strong>of</strong> <strong>earnings</strong> management between a<br />

sample <strong>of</strong> non-U.S. firms that are cross-listed in the U.S. and a sample <strong>of</strong> U.S. firms. They<br />

document that the cross-listed non-U.S. firms exhibit more evidence <strong>of</strong> smoothness, a greater<br />

tendency to report small pr<strong>of</strong>its, and less timely recognition <strong>of</strong> losses, and lower ERCs than U.S.<br />

firms and that this difference is greater for firms from countries with poor investor protection. 40<br />

Lang, Raedy, and Wilson (2006) do not use the Leuz et al. (2003) measure <strong>of</strong> artificial smoothness;<br />

they develop their own measure <strong>of</strong> artificial smoothness that similarly attempts to control for<br />

smoothness <strong>of</strong> the fundamental <strong>earnings</strong> process. They measure smoothness as the volatility <strong>of</strong><br />

<strong>earnings</strong> scaled by total assets, where “volatility” is the variance <strong>of</strong> the residuals from a regression <strong>of</strong><br />

annual scaled changes in net income on control variables for fundamental firm characteristics. Their<br />

analysis also uses a matched sample design with matching based on past sales growth and industry,<br />

which is an effort to control for fundamental variability.<br />

Three later studies extend Leuz et al. by providing evidence on how the interactions between<br />

different institutional factors influence the extent <strong>of</strong> <strong>earnings</strong> management. Haw et al. (2004)<br />

document that <strong>earnings</strong> management (measured by the unsigned magnitude <strong>of</strong> discretionary<br />

40 They use ERCs as one <strong>of</strong> their <strong>earnings</strong> management proxies.<br />

77

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