The Dangers of Misclassifying “Employees”: Microsoft Litigation Emphasizes Distinctions Between Employees and Nontraditional Workers

In the past decade, businesses, for reasons such as greater flexibility and lower total costs, have increased their use of nontraditional workers such as independent contractors and temporary agency employees. The Internal Revenue Service's crackdown on misclassifying employees slowed this growth. Additionally, recent lawsuits by nontraditional workers against employers such as Microsoft Corporation have resulted in some client employers being forced to pay more benefits to nontraditional employees because of employment misclassification and coemployment rulings. Such rulings might reduce or even eliminate the use of some types of nontraditional employees. This paper analyzes the vast array of types of workers that have evolved into the nontraditional work force and the difficulty of defining employee with respect to entitlement to employer benefits. Possible recommendations for employers include auditing present use of nontraditional workers, accurately classifying employees, and taking proper actions to minimize problems in the future.     

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data

In this article, we construct two likelihood‐based confidence intervals (CIs) for a binomial proportion parameter using a double‐sampling scheme with misclassified binary data. We utilize an easy‐to‐implement closed‐form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full‐likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close‐to‐nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real‐data example.     

On a 2 × 2 factorial design where the use of randomized response is one of the factors

This paper discusses a survey where some respondents were asked sensitive questions directly and others were asked the same questions using randomized response. The use of randomized response was a factor in a 2 × 2 factorial design and dice were used to perform the randomization. First, the paper shows that the perturbation due to the dice can be described using the concept of misclassification and known conditional misclassification probabilities. Second, the paper formulates the likelihood for loglinear models and shows that latent class software can be used to analyse the data. An example including a power analysis is discussed.     

Auditors’ going concern reporting accuracy during and after the global financial crisis

Motivated by concerns that one of the reasons for the Global Financial Crisis (GFC) is poor quality auditing, this study examines the accuracy of going concern modifications for a sample of United States (U.S.) companies in the pre-GFC (2005–2006), GFC (2007–2008), and post-GFC (2009–2010) periods. The results show that the type I misclassification is lower during the GFC but not different in the post-GFC period compared with the pre-GFC period. The type II misclassification is not significantly different in the GFC and post-GFC periods compared with the pre-GFC period. Additionally, the results suggest that non-Big 4 auditors, compared with Big 4 auditors, have become more conservative on clients’ going concern problems in the post-GFC period, which reduces their type II misclassification.     

Employment and education discrimination against disabled people in Cape Verde

This article assesses the employment and school enrolment gaps between disabled and nondisabled persons using the last Cape Verdean census. The unexplained part of these gaps accounts for most of them, whatever the age group considered. Furthermore, differences in age structures between disabled and nondisabled persons have almost no effect on these gaps. Taking into account potential misclassification errors in the disability variable seems to change only marginally these results. These findings thus suggest that there is scope for programs to better target and promote employment and education of the disabled in Cape Verde.     

Errors of misclassification in discrimination of dimensional coherent elliptic random field observations

The problem of classification of dimensional coherent elliptic random field observations into one of two populations specified by different regression mean models and common stationary scale matrix is considered, under the further assumption that the observations to be classified are dependent on the training samples. In this statistical frame, the behaviour of linear discriminant function is studied and an asymptotic expression for the distribution function of the probabilities of misclassification is derived.     

Finance capital in dynamic factor-demand models

Researchers often use the discrepancy between self-reported and biochemically assessed active smoking status to argue that self-reported smoking status is not reliable, ignoring the limitations of biochemically assessed measures and treating it as the gold standard in their comparisons. Here, we employ econometric techniques to compare the accuracy of self-reported and biochemically assessed current tobacco use, taking into account measurement errors with both methods. Our approach allows estimating and comparing the sensitivity and specificity of each measure without directly observing true smoking status. The results, robust to several alternative specifications, suggest that there is no clear reason to think that one measure dominates the other in accuracy.     

Mortgage Default: Classification Trees Analysis

We apply the powerful, flexible, and computationally efficient nonparametric Classification and Regression Trees (CART) algorithm to analyze real estate mortgage data. CART is particularly appropriate for our data set because of its strengths in dealing with large data sets, high dimensionality, mixed data types, missing data, different relationships between variables in different parts of the measurement space, and outliers. Moreover, CART is intuitive and easy to interpret and implement. We discuss the pros and cons of CART in relation to traditional methods such as linear logistic regression, nonparametric additive logistic regression, discriminant analysis, partial least squares classification, and neural networks, with particular emphasis on real estate. We use CART to produce the first academic study of Israeli mortgage default data. We find that borrowers features, rather than mortgage contract features, are the strongest predictors of default if accepting icbadli borrowers is more costly than rejecting good ones. If the costs are equal, mortgage features are used as well. The higher (lower) the ratio of misclassification costs of bad risks versus good ones, the lower (higher) are the resulting misclassification rates of bad risks and the higher (lower) are the misclassification rates of good ones. This is consistent with real-world rejection of good risks in an attempt to avoid bad ones.     

A Bayesian approach to misclassified binary response: female employment and intimate partner violence in urban India

We examine the effect of female employment on the odds of physical spousal violence using a Bayesian misclassification model combined with propensity score regression estimation. While a classical propensity score model finds a significant violence-provoking effect of female employment, our model finds no evidence of a significant effect. This suggests that misleading inferences are caused by falsely small standard errors in a model that does not account for uncertainties around propensity scores. Further, we confirm our misclassification model as a preferred specification using Deviance Information Criterion (DIC).     

Bayesian interval estimation for the difference of two independent Poisson rates using data subject to under‐reporting

Comparing occurrence rates of events of interest in science, business, and medicine is an important topic. Because count data are often under‐reported, we desire to account for this error in the response when constructing interval estimators. In this article, we derive a Bayesian interval for the difference of two Poisson rates when counts are potentially under‐reported. The under‐reporting causes a lack of identifiability. Here, we use informative priors to construct a credible interval for the difference of two Poisson rate parameters with under‐reported data. We demonstrate the efficacy of our new interval estimates using a real data example. We also investigate the performance of our newly derived Bayesian approach via simulation and examine the impact of various informative priors on the new interval.