Recent theory argues that concern about job security creates an incentive for managers to smooth earnings in consideration of both current and future relative performance. We find support for this theory. Our evidence suggests that when current earnings are ‘poor’ and expected future earnings are ‘good’, managers ‘borrow’ earnings from the future for use in the current period. Conversely, when current earnings are ‘good’ and expected future earnings are ‘poor’ managers ‘save’ current earnings for possible use in the future. However, sensitivity analysis indicates that we cannot rule out selection bias as a potential alternative explanation for our findings.

Abstract. We examine the empirical relationship between auditors' resource allocations and selected engagement characteristics. Our measure of resources is hours of grades of labor (partner, manager, etc.) “charged” to audit activities (planning, internal control evaluation, etc.). Engagement characteristics examined are client size, industry affiliation, client complexity, risk, auditor provision of management advisory services to the auditee, and degree of control reliance. The data were obtained from publicly available sources and a survey developed and administered by an international public accounting firm. We find the cross‐sectional variation in the labor charged to various audit activities can be explained by engagement characteristics found to be important in prior studies on audit fees, total labor inputs, and the mix of labor inputs. Measures of client size, industry, complexity, risk, and services provided are associated with changes in the allocation of labor among audit activities. We find no substitution of internal control review/testing for substantive testing on reliance audits. Task assignments vary by rank. Measures of client size, complexity, risk, and services provided are associated with activity‐specific changes in the labor mix.

To obtain consistency and asymptotic normality, a generalized method of moments (GMM) estimator typically is defined to be an approximate global minimizer of a GMM criterion function. To compute such an estimator, however, can be problematic because of the difficulty of global optimization. In consequence, practitioners usually ignore the problem and take the GMM estimator to be the result of a local optimization algorithm. This yields an estimator that is not necessarily consistent and asymptotically normal. The use of a local optimization algorithm also can run into the problem of instability due to flats or ridges in the criterion function, which makes it difficult to know when to stop the algorithm. To alleviate these problems of global and local optimization, we propose a stopping-rule (SR) procedure for computing GMM estimators. The SR procedure eliminates the need for global search with high probability. And, it provides an explicit SR for problems of stability that may arise with local optimization problems.

This paper introduces a conditional Kolmogorov test of model specification for parametric models with covariates (regressors). The test is an extension of the Kolmogorov test of goodness-of-fit for distribution functions. The test is shown to have power against 1/√n local alternatives and all fixed alternatives to the null hypothesis. A parametric bootstrap procedure is used to obtain critical values for the test.

In this paper I discuss alternatives to the GMM estimators proposed by Hansen (1982) and others. These estimators are shown to have a number of advantages. First of all, there is no need to estimate in an initial step a weight matrix as required in the conventional estimation procedure. Second, it is straightforward to derive the distribution of the estimator under general misspecification. Third, some of the alternative estimators have appealing information-theoretic interpretations. In particular, one of the estimators is an empirical likelihood estimator with an interpretation as a discrete support maximum likelihood estimator. Fourth, in an empirical example one of the new estimators is shown to perform better than the conventional estimators. Finally, the new estimators make it easier for the researcher to get better approximations to their distributions using saddlepoint approximations. The main cost is computational: the system of equations that has to be solved is of greater dimension than the number of parameters of interest. In practice this may or may not be a problem in particular applications.

This paper investigates systematic changes in the value-relevance of earnings and book values over time. We report three primary findings. First, contrary to claims in the professional literature, the combined value-relevance of earnings and book values has not declined over the past forty years and, in fact, appears to have increased slightly. Second, while the incremental value-relevance of ‘bottom line’ earnings has declined, it has been replaced by increasing value-relevance of book values. Finally, much of the shift in value-relevance fiom earnings to book values can be explained by the increasing frequency and magnitude of one-time items, the increasing frequency of negative earnings, and changes in average firm size and intangible intensity across time.

Most previous studies of the dynamics of industry structure, by emphasizing changes in concentration, conceal much of the nature of underlying competitive processes. Here we employ a stochastic firm growth model, estimated on U.K. data of 1979–1986 for over 200 leading firms, to derive joint predictions about the stability of market shares and the change of concentration. We find that changes in the market shares of surviving firms are the dominant influence on concentration, which is typically fairly stable in spite of considerable market-share turbulence. Advertising plays a major role in the dynamics of market shares and, therefore, affects both concentration and turbulence.