Rural Residence as a Determinant of Attitudes Toward US Immigration Policy

Between 1990 and 2000, the foreign-born population in the United States increased by 57 per cent, compared to a 13 per cent increase in the native-born population. This growth has fueled considerable media attention and has fomented some anti-immigrant sentiments. Although a number of authors have charted changes in support for restrictionist immigration policies, few have examined their determinants. In this paper, we focus on region of residence and use data from a 2004 telephone survey of a nationally representative sample of 1888 adults to test the hypothesis that rural Americans are more likely to support restrictive immigration policies than individuals in urban and suburban communities. In a series of regression analyses, this hypothesis is confirmed; rural residents hold the most restrictionist views. Additional analyses indicate that this effect of rural residence on policy attitudes is mediated by attitudes toward multiculturalism, the perceived traits of immigrants, and perceptions about the costs of immigration. Ultimately, the analyses indicate that the perceived cost of immigration is the single strongest predictor of support for restrictive immigration policies, and that it best accounts for rural residents'more restrictionist views.     

Quantitative comparisons between finitary posterior distributions and Bayesian posterior distributions

The main object of Bayesian statistical inference is the determination of posterior distributions. Sometimes these laws are given for quantities devoid of empirical value. This serious drawback vanishes when one confines oneself to considering a finite horizon framework. However, assuming infinite exchangeability gives rise to fairly tractable a posteriori quantities, which is very attractive in applications. Hence, with a view to a reconciliation between these two aspects of the Bayesian way of reasoning, in this paper we provide quantitative comparisons between posterior distributions of finitary parameters and posterior distributions of allied parameters appearing in usual statistical models.     

Collective consumption: an application to the passive drinking effect

This study introduces a new collective complete demand system with individual Engel effects that is easy to estimate and permits undertaking policy analysis at the individual rather than household level. Previous estimations of collective demand were limited to single equations. The empirical application investigates the passive drinking effect, that is, whether consumption of alcohol affects the distribution of resources among household members and their level of wellbeing. The results show that a high level of alcohol consumption of one household member significantly affects the allocation of household resources and suggest thought-provoking policy implications.     

A set of independent sequential residuals for the multivariate regression model

In this paper a set of residuals for the multivariate linear regression model is introduced. These residuals are shown to be independent with known distributions which do not depend on the parameters of the model. Transformations of the mentioned residuals may be used to construct exact α goodness-of-fit tests for the multivariate regression model.     

Training x Trainee Interactions in a family planning intervention

This study investigated the effects of introducing a family planning counseling model at clinics of Peru's Ministry of Health. Providers trained in the model presented greater quality of care and longer counseling sessions than did controls. The main effects, however, were misleading. Nearly all of the quality improvements were contributed by 37% of the trained providers; they had already been better performers at the pretest and complied with the new counseling model's requirement of job aid use at the posttest. The recognition that a majority of trained providers did not profit from training posed a challenge that led to improvements in the model. Training x Trainee Interactions should be explored in evaluations.     

On the finite-sample properties of conditional empirical likelihood estimators

We provide Monte Carlo evidence on the finite-sample behavior of the conditional empirical likelihood (CEL) estimator of Kitamura, Tripathi, and Ahn and the conditional Euclidean empirical likelihood (CEEL) estimator of Antoine, Bonnal, and Renault in the context of a heteroscedastic linear model with an endogenous regressor. We compare these estimators with three heteroscedasticity-consistent instrument-based estimators and the Donald, Imbens, and Newey estimator in terms of various performance measures. Our results suggest that the CEL and CEEL with fixed bandwidths may suffer from the no-moment problem, similarly to the unconditional generalized empirical likelihood estimators studied by Guggenberger. We also study the CEL and CEEL estimators with automatic bandwidths selected through cross-validation. We do not find evidence that these suffer from the no-moment problem. When the instruments are weak, we find CEL and CEEL to have finite-sample properties—in terms of mean squared error and coverage probability of confidence intervals—poorer than the heteroscedasticity-consistent Fuller (HFUL) estimator. In the strong instruments case, the CEL and CEEL estimators with automatic bandwidths tend to outperform HFUL in terms of mean squared error, while the reverse holds in terms of the coverage probability, although the differences in numerical performance are rather small.     

Describing extra-binomial variation with partially correlated models

Data in the form of proportions are often analyzed under a binomial model. However, because genuine random sampling is often infeasible, the subjects in the sample may be collected in clumps and the variances of the observed proportions may be considerably larger than those corresponding to the binomial model. A set of data from a study of the proportion of subjects testing positive to the disease toxoplasmosis is used in this article to motivate partially correlated binomial models capable of describing data observed in practical situations where clumped sampling is likely to appear, According to these models, the extra-binomial variance of the observed frequencies may range from a linear to a quadratic function of the sample size. An efficient algorithm for the evaluation of the resulting probability mass function is given.     

Public Sector Strategies in Curbing Corruption: A Review of the Literature

Public Organization Review - Corruption is widespread and preventive strategies to reduce corruption need to be adapted within the local context. Considering the United Nations (UN) Convention...