Racial Identity and Racial Treatment of Mexican Americans

How racial barriers play in the experiences of Mexican Americans has been hotly debated. Some consider Mexican Americans similar to European Americans of a century ago that arrived in the United States with modest backgrounds but were eventually able to participate fully in society. In contrast, others argue that Mexican Americans have been racialized throughout U.S. history, and this limits their participation in society. The evidence of persistent educational disadvantages across generations and frequent reports of discrimination and stereotyping supports the racialization argument. In this paper, we explore the ways in which race plays a role in the lives of Mexican Americans by examining how education, racial characteristics, social interactions, relate to racial outcomes. We use the Mexican American Study Project, a unique data set based on a 1965 survey of Mexican Americans in Los Angeles and San Antonio combined with surveys of the same respondents and their adult children in 2000, thereby creating a longitudinal and intergenerational data set. First, we found that darker Mexican Americans, therefore appearing more stereotypically Mexican, report more experiences of discrimination. Second, darker men report much more discrimination than lighter men and than women overall. Third, more educated Mexican Americans experience more stereotyping and discrimination than their less educated counterparts, which is partly due to their greater contact with whites. Lastly, having greater contact with whites leads to experiencing more stereotyping and discrimination. Our results are indicative of the ways in which Mexican Americans are racialized in the United States.     

Bayesian reconstruction of contingency tables with partially categorized data

This paper suggests a Bayesian approach to the reconstruction of a 2 × 2 contingency table where some of the observations are only partially categorized and others are fully categorized. In contrast, most previous Bayesian and non-Bayesian analyses of the partially categorized data problem have been concerned with estimation of the parameters that generated the data. We show in an example that estimates may not be extremely sensitive to the weight placed on prior information relative to the sample data.     

Book Reviews

The diagnostic tools examined in this article are applicable to regressions estimated with panel data or cross-sectional data drawn from a population with grouped structure. The diagnostic tools considered include (a) tests for the existence of group effects under both fixed and random effects models, (b) checks for outlying groups, and (c) specification tests for comparing the fixed and random effects models. A group-specific counterpart to the studentized residual is introduced. The methods are illustrated using a hedonic housing price regression.     

Parsimonious Estimation of the Covariance Matrix in Multinomial Probit Models

This article presents a Bayesian analysis of a multinomial probit model by building on previous work that specified priors on identified parameters. The main contribution of our article is to propose a prior on the covariance matrix of the latent utilities that permits elements of the inverse of the covariance matrix to be identically zero. This allows a parsimonious representation of the covariance matrix when such parsimony exists. The methodology is applied to both simulated and real data, and its ability to obtain more efficient estimators of the covariance matrix and regression coefficients is assessed using simulated data.     

The Ranges of Limiting Values of Some Partial Correlations under Conditional Independence

Pearson's partial correlation, Kendall's partial tau, and a partial correlation based on Spearman's rho need not be consistent estimators of zero under conditional independence. The ranges of possible limiting values of these correlations are computed under multivariate normality and lognormality. Students should exercise caution when interpreting these partial correlations as a measure of conditional independence.