A DEA-based stochastic estimation framework is presented to evaluate contextual variables affecting productivity. Conditions are identified under which a two-stage procedure consisting of DEA followed by regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimation in the second stage yields consistent estimators of the impact of contextual variables. Monte Carlo simulations are carried out to compare the performance of our two-stage approach with one-stage and two-stage parametric approaches. Simulation results suggest that DEA-based procedures perform as well as the best parametric method in the estimation of the impact of contextual variables on productivity. Simulation results also indicate that DEA-based procedures perform better than parametric methods in the estimation of individual decision making unit (DMU) productivity.

We present evidence on components of productivity change in the public accounting industry toward the end of the 20th century. Using revenue and human resource data from 64 of the 100 largest public accounting firms in the United States for the 1995--1999 period, we analyze productivity change, technical progress, and relative efficiency change over time. The average public accounting firm experienced a productivity growth of 9.5% between 1995 and 1999. We find support for the hypothesis that technical progress rather than an improvement in relative efficiency was the reason for this productivity growth. Firms that were early movers into management advisory services (MAS) and those that emphasized growth in MAS over growth in the traditional audit and tax services enjoyed significantly higher productivity growth than their peers. These firms also contributed significantly more to the industry's technical progress.public accounting, productivity change, technical progress, relative efficiency, management advisory services, auditing, taxation services

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Accounting performance measures such as earnings and cash flows are useful for both valuation and performance evaluation purposes. However, little evidence exists on whether there is any association between these two roles. In this study, we provide large sample empirical evidence that the value relevance of earnings explains a significant amount of the cross‐sectional variation in the pay‐sensitivity of earnings and the incremental value relevance of cash flows explains variation in the marginal pay‐sensitivity of cash flows. We document that while both value relevance and compensation weight on earnings decline from the subperiod of 1993 to 1997 to the subperiod of 1998 to 2003, both value relevance and compensation weight on cash flows increase from the earlier subperiod to the later subperiod. Overall, our results provide additional evidence that value relevance of a performance measure plays a significant role in its use for performance evaluation.

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