*University of Southern California; tUniversity of Colorado at Boulder. We would like to thank Gilbert Bloom of KPMG Peat Marwick, Bob Rosen of Ernst & Young, Wayne Gazur, Robert Jamison, Sally Jones, Stewart Karlinsky, and David Mason for their assistance in validating the instruments; Eugene Willis and the AICPA for allowing us to collect data at the National Tax Education Program; Stephen Conrad of Arthur Andersen, John Lanning of KPMG Peat Marwick, Jerry Marrs of Ernst & Young, and Randy Stein of Coopers & Lybrand for allowing us to collect data at their respective firms; Minou Bohlin, Linda Levy, David Mason, and Paul Walker for their research assistance; and Vairum Arunachalam for his assistance in collecting data. The authors also gratefully acknowledge the helpful comments of three anonymous referees, Alison Ashton, Robert Ashton, C. Brian Cloyd, David Frederick, Joan Luft, Robert Libby, Laureen Maines, Mark Nelson, Michael Roberts, Frank Selto, D. Shores, Ira Solomon, Rick Tubbs, S. Mark Young, and workshop participants at Arizona State University, Cornell University, Duke University, Indiana University, the University of Illinois Tax Symposium, the Journal of Accounting Research Conference, University of Texas at Arlington, University of Utah, and University of Wisconsin. Finally, the financial support of the KPMG Peat Marwick Foundation and the University of Colorado is gratefully acknowledged. 1 We infer expertise in this study from the level of performance in a specific task, here issue identification in tax planning. This inference is consistent with much of the literature on expertise in accounting and other disciplines (e.g., Bonner and Lewis [1990],

Lease valuation models often begin with the assumption that leases and debt are substitutes. This paper demonstrates that, because leasing is a mechanism for selling excess tax deductions, it can motivate the lessee firm to increase the proportion of debt in its capital structure relative to an otherwise identical firm that does not use leasing. Thus, debt and leases can be complements. We also show that a competitive lessor will use diversification to reduce risk and increase the probability that tax deductions are fully utilized so that it can lower lease payments.

This paper examines the extent to which aggregate stock return variation is explained by variables chosen to reflect revisions in expectations of future dividends. In effect, we decompose realized dividend growth into expected and unexpected components using information in aggregate investment, dividend yield, and future returns. A parsimonious specification accounts for over 70% of annual return variation. We also conduct a cross-sectional experiment using portfolios formed on the basis of annual return performance. This analysis shows that nearly 90% of the portfolio return variation is explained by dividend and expected return variables.

Teen childbearing is commonly believed to cause long-term socioeconomic disadvantages for mothers and their children. However, earlier cross-sectional studies may have inadequately accounted for marked differences in family background among women who have first births at different ages. We present new estimates that take into account unmeasured family background heterogeneity by comparing sisters who timed their first births at different ages. In two of the three data sets we examine, sister comparisons suggest that biases from family background heterogeneity are important, and, therefore, that earlier studies may have overstated the consequences of teen childbearing.

Abstract In the last two decades of accounting research, many studies have investigated the relation between accounting variables and risk-adjusted security returns. The earliest studies (e.g., Ball and Brown 1968) used simple, nonparametric methods and focused mainly on the question of whether accounting earnings are associated with residual equity returns. Subsequent studies made methodological refinements in both the measurement and the statistical techniques. One important statistical refinement was the "unexpected earnings response regression model" (UERRM), a linear statistical model that uses the unexpected earnings variable as a regressor to explain risk-adjusted returns.' The UERRM has become well-known, and many recent studies (e.g., Cornell and Landsman 1989, 686; Daley et al. 1988, 580; Doran et al. 1988, 392; Landsman and Damodaran 1989, 107; McNichols 1989, 15) have used some form of it as a "benchmark" model, against which to compare more complicated models. In one paradigm (so popular that it has become almost standard practice), various accounting variables are added to the UERRM, and their "incremental information content" is assessed by testing the statistical significance of their coefficients. The inferences derived from this procedure are, of course, conditional on the degree to which the UERRM is correctly specified. A critical problem caused by using a misspecified UERRM is that its least squares estimator can lead to erroneous inferences in the research design. Despite the UERRM's popularity, researchers have expressed concerns regarding its specification. For example, Lev (1989) recently surveyed a large number of research papers that used some form of an UERRM and found that for the most part R2s were low and often bordered on "the negligible." His table 1, which includes statistics from 19 studies, indicates that most reported R²s are less than 10 percent. Although low R²s are not proof of major specification problems, Lev does suggest that they are a cause for concern and may be the result of specification problems. Investigation of multiple specification problems is an important aspect of the present study because the various specification issues are interrelated. For example, nonnormality can be associated with nonlinearity, which, in turn, can be associated with heteroscedasticity or variation in the coefficients (Judge et al. 1985, 455, 814, 839). Thus, ad hoc tests for a single specification problem can be misleading and can fail to identify the fundamental problems. To our knowledge, no studies have comprehensively and formally evaluated the specification of the cross-sectional, ordinary least squares model that relates unexpected earnings to risk-adjusted security returns. Therefore, the purpose of this study is to test such a model systematically and empirically for specification problems Specifically, this study tests for nonlinearity, heteroscedasticity, residual nonnormality, omitted variables, and interfirm systematic and random coefficient variation. Also, when appropriate, adjusted R²-statistics are included to indicate the degree of misspecification information that is not directly observable from the tests themselves. A high degree of generality is obtained by using three samples of earnings forecasts as proxies for expected earnings. These were obtained from IBES financial analyst consensus forecasts, Value Line financial analyst forecasts, and COMPUSTAT-based time-series forecasts. Daily and monthly security returns are considered for short and long event-windows, respectively. In addition, one study recently published in The Accounting Review (Cornell and Landsman 1989) is replicated. The findings from all of the samples and the replication indicate that the specification error is large enough to affect conclusions regarding economic relationships. For example, in the replication, the specification problems are shown to lead to substantial instability in inferences from the model.

Abstract Presents an overview on accounting for defense contracts. Opportunities for accounting scholarship in the defense industry; Regulation in the industry; Possibility of efficient behavior in the industry; Contracting frictions; Product costing's impact on factor choice incentives.