This paper examines the change in the average level of moral development over a 7.5‐year period of promotion, attrition, and survival in five Big 6 firms. The study improves upon previous cross‐sectional studies that found decreases in the average level of moral development at the senior manager and partner levels, which has been referred to as the “inverted‐U” phenomenon. Problems with these studies that limit the generalizability of their findings include their cross‐sectional nature and samples that usually come from one or two firms. Over a 7.5‐year period, we found that the participating Big 6 firms retained auditors with higher average levels of moral development (measured using the defining issues test), while those with lower average levels left the firms. The average level of moral development for new partners was at least as high as the group from which they came. This research suggests that the concern about Big 6 firms retaining a higher proportion of auditors with lower moral development may be an artifact of research design.

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block–block bootstrap. The asymptotic refinements of the block–block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.

The local Whittle (or Gaussian semiparametric) estimator of long range dependence, proposed by Künsch (1987) and analyzed by Robinson (1995a), has a relatively slow rate of convergence and a finite sample bias that can be large. In this paper, we generalize the local Whittle estimator to circumvent these problems. Instead of approximating the short-run component of the spectrum, ϕ(λ) , by a constant in a shrinking neighborhood of frequency zero, we approximate its logarithm by a polynomial. This leads to a "local polynomial Whittle" (LPW) estimator. We specify a data-dependent adaptive procedure that adjusts the degree of the polynomial to the smoothness of ϕ(λ) at zero and selects the bandwidth. The resulting "adaptive LPW" estimator is shown to achieve the optimal rate of convergence, which depends on the smoothness of ϕ(λ) at zero, up to a logarithmic factor. Copyright The Econometric Society 2004.

Wage hedonic models are estimated with the Health and Retirement Study to measure the risk-wage tradeoffs (value of statistical lives) for older workers. The analysis explicitly allows for multiple employment states, including retirement, using a multinomial selection model. The results suggest that the oldest and most risk-averse workers require significantly higher, not lower, compensation to accept increases in job-related fatality risks. 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology. Classification-JEL: I12