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This study applies a new taxonomy of racial/ethnic misclassification that considers shifts in racial/ethnic status to investigate physical and emotional responses to racial treatment among different misclassification types. It finds that the odds of reporting physical and emotional symptoms increase 3.3 and 2.9 times, respectively, among individuals who experience racial/ethnic status loss (i.e., are misclassified into a racial/ethnic category with lower status compared to their self-reported category) compared to their correctly classified counterparts. In contrast, individuals who experience racial/ethnic status gain (i.e., are misclassified into a racial/ethnic category with higher status compared to their self-reported category) are no more likely to suffer from symptoms compared to correctly classified individuals. The results suggest that being misclassified per se does not necessarily harm well-being, but the loss of social status inherent in some types of misclassification does.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.

Industrial relations, organizational behavior, and human resource management scholars have studied numerous aspects of internal workplace conflict resolution, ranging from the design of conflict resolution systems to the processes used for resolving conflicts to the outcomes of the systems. Scholars from these specialties, however, have paid considerably less attention to external workplace conflict resolution through litigation. This chapter analyzes certain areas of such litigation, focusing specifically on workplace conflicts involving issues of managerial and employee misclassification, independent contractor versus employee status, no-poaching agreements, and executive compensation.

Leading recent cases involving these issues are examined, with particular attention given to the question of whether the conflicts reflected therein could have been resolved internally or through alternative dispute resolution (ADR) methods rather than through litigation.

Implications of this analysis are drawn for workplace conflict resolution theory and practice. In doing so, I conclude that misclassification disputes could likely be resolved internally or through ADR rather than through litigation, but that no-poaching and executive compensation disputes could very likely not be resolved internally or through ADR.

The chapter draws on and offers an integrated analysis of particular types of workplace conflict that are typically treated separately by scholars and practitioners. These include misclassification conflicts, no poaching and labor market competition conflicts, and executive compensation conflicts. The originality and value of this chapter are to show that despite their different contexts and particular issues, the attempted resolution through litigation of these types of workplace conflicts has certain common, systematic characteristics.

Researchers in economics and other disciplines are often interested in the causal effect of a binary treatment on outcomes. Econometric methods used to estimate such effects are divided into one of two strands depending on whether they require unconfoundedness (i.e., independence of potential outcomes and treatment assignment conditional on a set of observable covariates). When this assumption holds, researchers now have a wide array of estimation techniques from which to choose. However, very little is known about their performance – both in absolute and relative terms – when measurement error is present. In this study, the performance of several estimators that require unconfoundedness, as well as some that do not, are evaluated in a Monte Carlo study. In all cases, the data-generating process is such that unconfoundedness holds with the ‘real’ data. However, measurement error is then introduced. Specifically, three types of measurement error are considered: (i) errors in treatment assignment, (ii) errors in the outcome, and (iii) errors in the vector of covariates. Recommendations for researchers are provided.

Classifying workers as either employees or independent contractors has significant financial consequences for the payer (usually a business) and the worker. The payer may be motivated more by the desire to avoid paying for employee benefits and employer payroll taxes than by doing the right thing and correctly classifying and paying the worker as an employee. Estimates are that the cost of such benefits and taxes may equal 20–30% of gross pay. When governmental regulations are unclear or enforcement is lax, many stakeholders suffer. This includes the workers, their families, their co-workers, and law-abiding employers as well as citizens (taxpayers) who must pay more than their fair share to provide adequate funding for related government programs and benefits. This is a global issue as evidenced by widely publicized lawsuits in many countries involving prominent defendants such as Microsoft, Uber, and Lyft. Software platforms used to distribute small jobs to temporary and part-time workers have resulted in the exponential growth of the gig economy. Such technology has also further enabled the misclassification of workers beyond what has occurred in years past. An ethical analysis to identify the many stakeholders and the impact of worker misclassification should be conducted to guide governments in developing and enhancing regulations for the pervasive issue of worker classification and to protect the rights of their workers and taxpayers.

This chapter presents a model of distribution dynamics in the presence of measurement error in the underlying data. Studies of international growth convergence generally ignore the fact that per capita income data from the Penn World Table (PWT) are not only continuous variables but also measured with error. Together with short-time scale fluctuations, measurement error makes inferences potentially unreliable. When first-order, time-homogeneous Markov models are fitted to continuous data with measurement error, a bias towards excess mobility is introduced into the estimated transition probability matrix. This chapter evaluates different methods of accounting for this error. An EM algorithm is used for parameter estimation, and the methods are illustrated using data from the PWT Mark 6.1. Measurement error in income data is found to have quantitatively important effects on distribution dynamics. For instance, purging the data of measurement error reduces estimated transition intensities by between one- and four-fifths and more than halves the observed mobility of countries.

Prior analyses of racial bias in the New York City’s Stop-and-Frisk program implicitly assumed that potential bias of police officers did not vary by crime type and that their decision of which type of crime to report as the basis for the stop did not exhibit any bias. In this paper, we first extend the hit rates model to consider crime type heterogeneity in racial bias and police officer decisions of reported crime type. Second, we reevaluate the program while accounting for heterogeneity in bias along crime types and for the sample selection which may arise from conditioning on crime type. We present evidence that differences in biases across crime types are substantial and specification tests support incorporating corrections for selective crime reporting. However, the main findings on racial bias do not differ sharply once accounting for this choice-based selection.

Several prominent data-mining studies have evaluated the performance of neural networks (NNs) against traditional statistical methods on the two-group classification problem in discriminant analysis. Although NNs often outperform traditional statistical methods, their performance can be hindered because of failings in the use of training data. This problem is particularly acute when using NNs on smaller data sets. A heuristic is presented that utilizes Mahalanobis distance measures (MDM) to deterministically partition training data so that the resulting NN models are less prone to overfitting. We show this heuristic produces classification results that are more accurate, on average, than traditional NNs and MDM.

This paper posits that legal avoidance – employers’ search for forms of employment to which labor and employment laws do not apply – is an important driver of the restructuring of work. It examines three examples of restructuring that enable employers to avoid legal liability and compliance costs: the classification of workers as independent contractors; the use of part-time and variable-schedule work; and employers’ deskilling of jobs and reliance on vulnerable workers. None of these strategies is itself unlawful, but their impact is to limit workers’ legal protections and weaken the law itself. Employers may also experience unintended consequences of restructuring.