Testing the Effects of Collectively Expected Durations of Migration: The Naturalization of Mexicans and Cubans

This study tests a hypothesis that Mexican foreign‐born immigrants who came to the United States for economic reasons naturalize less often than Cubans who immigrate for political reasons. It uses information from the Panel Study of Income Dynamics, Latino Sample, a national sample of 7,453 respondents from the 1989 Latino National Political Survey (LNPS) and the 1990 Panel Study of Income Dynamics (PSID). Ordinal logistic regression is used to examine the hypothesis. The results indicate that while more Mexicans plan to apply or have applied for naturalization, proportionately more Cubans than Mexicans have naturalized. Cuban political immigrants who came to the United States during the first half of the 1960s naturalize more often than their Mexican counterparts. However, the effect of ethnic identity on naturalization is mediated by a number of other predictors of naturalization such as gender, race, urban residence, socioeconomic status and acculturation.     

Minority Poverty in Nonmetropolitan Texas*

Abstract Despite a high prevalence of poverty among minorities in nonmetropolitan areas, research and policy concerns regarding poverty have continued focusing on metropolitan minorities. This study uses a model integrating individual, household, and structural factors to examine poverty among Latinos, blacks, and Anglos in nonmetropolitan and, for comparative purposes, metropolitan areas, using data from the 1985 Special Texas Census TDHS 1987. The findings show that minorities in nonmetropolitan areas tend to have the highest poverty rates. In addition, consistent as well as divergent patterns exist among the six ethnic-resident groups with respect to the relationships among the various individual, household, and structural factors and poverty.     

Compliance Versus Risk in Assessing Occupational Exposures

Assessments of occupational exposures to chemicals are generally based upon the practice of compliance testing in which the probability of compliance is related to the exceedance [γ, the likelihood that any measurement would exceed an occupational exposure limit (OEL)] and the number of measurements obtained. On the other hand, workers’ chronic health risks generally depend upon cumulative lifetime exposures which are not directly related to the probability of compliance. In this paper we define the probability of “overexposure” (θ) as the likelihood that individual risk (a function of cumulative exposure) exceeds the risk inherent in the OEL (a function of the OEL and duration of exposure). We regard θ as a relevant measure of individual risk for chemicals, such as carcinogens, which produce chronic effects after long-term exposures but not necessarily for acutely-toxic substances which can produce effects relatively quickly. We apply a random-effects model to data from 179 groups of workers, exposed to a variety of chemical agents, and obtain parameter estimates for the group mean exposure and the within- and between-worker components of variance. These estimates are then combined with OELs to generate estimates of γ and θ. We show that compliance testing can significantly underestimate the health risk when sample sizes are small. That is, there can be large probabilities of compliance with typical sample sizes, despite the fact that large proportions of the working population have individual risks greater than the risk inherent in the OEL. We demonstrate further that, because the relationship between θ and γ depends upon the within- and between-worker components of variance, it cannot be assumed a priori that exceedance is a conservative surrogate for overexposure. Thus, we conclude that assessment practices which focus upon either compliance or exceedance are problematic and recommend that employers evaluate exposures relative to the probabilities of overexposure.     

An Empirical Approach to Sufficient Similarity: Combining Exposure Data and Mixtures Toxicology Data

When assessing risks posed by environmental chemical mixtures, whole mixture approaches are preferred to component approaches. When toxicological data on whole mixtures as they occur in the environment are not available, Environmental Protection Agency guidance states that toxicity data from a mixture considered “sufficiently similar” to the environmental mixture can serve as a surrogate. We propose a novel method to examine whether mixtures are sufficiently similar, when exposure data and mixture toxicity study data from at least one representative mixture are available. We define sufficient similarity using equivalence testing methodology comparing the distance between benchmark dose estimates for mixtures in both data‐rich and data‐poor cases. We construct a “similar mixtures risk indicator”(SMRI) (analogous to the hazard index) on sufficiently similar mixtures linking exposure data with mixtures toxicology data. The methods are illustrated using pyrethroid mixtures occurrence data collected in child care centers (CCC) and dose‐response data examining acute neurobehavioral effects of pyrethroid mixtures in rats. Our method shows that the mixtures from 90% of the CCCs were sufficiently similar to the dose‐response study mixture. Using exposure estimates for a hypothetical child, the 95th percentile of the (weighted) SMRI for these sufficiently similar mixtures was 0.20 (i.e., where SMRI <1, less concern; >1, more concern).     

The Nonmetro/metro Context of Racial/ethnic Outmarriage: Some Differences between African Americans and Mexican Americans1

Abstract Data from the 1990 U. S. Census are used to examine nonmetro-metro distinctions in the outmarriage patterns of the nation's two largest minority groups—African Americans and Mexican Americans. The analysis is guided by a multilevel model combining individual- and community-level determinants of outmarriage. Consistent with notions suggesting that persons in metro areas are less traditional and, perhaps, more tolerant of those different from them, we find that African Americans living in metro areas are more likely to be married to someone from another racial/ethnic group than their peers in nonmetro areas, even after residential differences in individual and community characteristics are taken into account. On the other hand, controlling for other factors, Mexican Americans living in metro areas are not any more likely than those living in nonmetro settings to be exogamous. One possible explanation for this divergent pattern is the relatively recent urbanization of the Mexican American population.     

Structural and Individual Determinants of Outmarriage Among Chinese-, Filipino-, and Japanese-Americans in California*

This study combines structural factors of intergroup relations with personal assimilation factors to examine Asian American intermarriage in California, We conduct a multilevel logistic regression analysis of outmarriage among Chinese, Filipinos, and Japanese in 14 California metropolitan statistical areas with high con centrations of Asian Americans by using the 1980 Census Public-Use Microdata Sample. The results show that Asian intermarriage is affected by both assimilation and structural determinants. Some theoretically unexpected findings are discussed that hint at alternative interpretations.     

A Matrix Variate Closed Skew-Normal Distribution with Applications to Stochastic Frontier Analysis

In this article, we introduce the matrix extension of the closed skew-normal distribution and give two constructions for it: a marginal one and another based on hidden truncation. Important basic properties of the distribution are presented such as its closure under linear transformation and moment generating function. We also give distributional results for quadratic forms involving random matrices distributed according to two particular cases of it. Using an additive construction, we derive a submodel which can be employed to describe the compound error structure of a very general multivariate stochastic frontier model. Finally, we consider the skew-elliptical extension of the proposed distribution.     

Traffic‐Based Labor Planning in Retail Stores

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.     

The human ecology of tornadoes

This paper offers an empirical test of the impact of human ecological patterns and other known correlates on tornado occurrence. It uses the National Severe Storms Forecast Center’s information on tornadoes from 1950 through 1990 and employs ecological data from the U.S. Bureau of the Census and the Environmental Protection Agency. The results show that metropolitan and other urban counties have higher odds of tornado occurrence than rural counties, and that the probability of occurrence of tornadoes increases with increases in the number of previous tornadoes. The paper assesses the meaning of this finding for demographers, atmospheric scientists, engineers, and disaster managers.