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We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.

The aim of this chapter is to analyze the sources of earnings dispersion between trainees and nontrainees. We stress three mechanisms by which investment in general training may affect wage inequality: directly via participation to a general training program and indirectly via the selection process of trainees or the existence of heterogeneous returns on training. This chapter adopts an approach originally proposed by Fields (2003) but extends it to the breakdown of inequality by population subgroups – those who received training and those who did not. The empirical illustration is based on four French surveys, the 2006 Adult Educational Survey and the 2004, 2005, and 2006 Labor Force Surveys that complement it.

We present Bayesian models for finding the longitudinal causal effects of a randomized two-arm training program when compliance with the randomized assignment is less than perfect in the training arm (but perfect in the non-training arm) for reasons that are potentially correlated with the outcomes. We deal with the latter confounding problem under the principal stratification framework of Sommer and Zeger (1991) and Frangakis and Rubin (1999), and others. Building on the Bayesian contributions of Imbens and Rubin (1997), Hirano et al. (2000), Yau and Little (2001) and in particular Chib (2007) and Chib and Jacobi (2007, 2008), we construct rich models of the potential outcome sequences (with and without random effects), show how informative priors can be reasonably formulated, and present tuned computational approaches for summarizing the posterior distribution. We also discuss the computation of the marginal likelihood for comparing various versions of our models. We find the causal effects of the observed intake from the predictive distribution of each potential outcome for compliers. These are calculated from the output of our estimation procedures. We illustrate the techniques and ideas with data from the 1994 JOBS II trial that was set up to test the efficacy of a job training program on subsequent mental health outcomes.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

In this paper, the gamma test is used to determine the order of lag-k tail dependence existing in financial time series. Using standardized return series, statistical evidences based on the test results show that jumps in returns are not transient. New time series models which combine a specific class of max-stable processes, Markov processes, and GARCH processes are proposed and used to model tail dependencies within asset returns. Estimators for parameters in the models are developed and proved to be consistent and asymptotically joint normal. These new models are tested on simulation examples and some real data, the S&P 500.

This chapter addresses quality management (QM) content on the process quality management (PQM) level in the high-technology industry of semiconductor manufacturing. Identifying critical components of a manufacturing or service process and improving them to ensure superior quality at economic costs is the overall goal of PQM. Deming was a prominent proponent of PQM as a means to optimize the performance of a product or process. In optimizing the performance of a product or process, good design practices require the evaluation of designs from a process perspective. Advanced design techniques, namely design of experiments (DOEs), are cornerstone to the optimization process, to design management, and in turn to PQM. This chapter investigates the use of DOEs in the manufacture of semiconductors. Specifically, two underlying assumptions impact operations managers using DOEs: solution differences/similarities in underlying DOE optimization methods and marginal rates of substitution. Perhaps unknown to the user, DOE optimization techniques carry strong assumptions regarding these characteristics. This chapter investigates two commonly used DOE optimization approaches applied to the operational control of semiconductor wafer production, and demonstrates that each method contains assumptions about these characteristics, which are not intuitively evident to a user.

In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).

This paper analyzes the effect of dental insurance on utilization of general dentist services by adult US population aged from 25 to 64 years using the ordered probit model with endogenous selection. Our econometric framework accommodates endogeneity of insurance and the ordered nature of the measure of dental utilization. The study finds strong evidence of endogeneity of dental insurance to utilization and identifies interesting patterns of nonlinear dependencies between the dental insurance status and individual's age and income. The calculated average treatment effect supports the claim of adverse selection into the treated (insured) state and indicates a strong positive incentives effect of dental insurance.

Excessive teacher entitlement results from interactions between and among social, institutional and individual elements. This study retrieves small entitlement stories of two teachers and one school principal from an online elementary teacher community in China. Through burrowing into the narrative data, this study presents how the three participants' selves are revealed in their interactions with different community members. The multi-layered relationships between entitlement, reflection, unknown self, personal/professional experiences, and teacher community are discussed. This study provides insights into how administrators, teachers and other educators can collaborate to foster teachers' self-understanding, with the purpose of ameliorating excessive teacher entitlement.

This paper analyzes labor market responses to productivity shocks when firms set employment criteria on the basis of the likelihood of hiring high or low productivity workers. In response to a positive productivity shock, firms do not raise the criterion as much as the shock, increasing the proportion of low productivity workers among the employed. The observed average productivity may respond negligibly even if employment changes substantially. Interest rate fluctuations can yield an opposite relation between productivity and employment, explaining the weak empirical relationship.