Buying into the American Dream? Mexican Immigrants,Legal Status,and Homeownership in Los Angeles County*

Objectives. Contemporary patterns of homeownership reflect the continuing racial and ethnic stratification that exists in nearly all areas of American society. Of particular interest, especially within the context of recent immigration legislation, are the homeownership experiences of Mexican immigrants in the United States. Methods. The current study employs unique data from the 2001 Los Angeles County Mexican Immigrant Residency Status Survey (LAC‐MIRSS) to examine the association between diverse forms of legal status and homeownership for Mexican immigrants. Results. Analyses indicate that the relationship between legal status and housing tenure is not statistically significant, after accounting for economic, life‐course/life‐cycle, and assimilation/social capital characteristics. Conclusions. The lack of a significant relationship is contrary to past research, perhaps explained by the explosive growth of the subprime mortgage market in the United States; the increasing recognition by financial institutions of Latino immigrants as a largely untapped, yet emerging, market in the mortgage industry; the availability of alternative forms of identification; and the institutionalization of unauthorized immigration in Los Angeles.     

Testing for programme effects in a regression discontinuity design with imperfect compliance

Summary. The administrators of an office automation training programme in Italy enrolled applicants on the basis of their score in an attitudinal test, with low scoring subjects mandated out of the programme. Some of the applicants who were mandated out resorted to an alternative programme. To identify the effect of the programme by comparing participants with non-participants we need to account properly for both the selection by the score and the contamination of the comparison group by a number of non-complying subjects. The estimand resulting from using the mandated status as an instrumental variable for the actual status identifies the effect of the programme on complying subjects exhibiting a score in the attitudinal test in the neighbourhood of the threshold for selection. Simple nonparametric instrumental variable estimators based on the work of Robinson and Hahn and co-workers reveal that the programme had no effect on the probability of being in work several months after its completion. Simulation results show that in spite of the small sample size the test for the no-impact hypothesis has non-negligible power even at small departures from the null hypothesis. As a side-result Robinson's test turns out to be appreciably more powerful than the other test.     

The Utility of Gambling Reconsidered

The utility of gambling, which entails an intrinsic utility or disutility of risk, has been alluded to in the economics literature for over a century. This paper demonstrates that any utility of gambling almost unavoidably implies a violation of fundamental rationality properties, such as transitivity or stochastic dominance, for static choices between gambles. This result may explain why the utility of gambling, a phenomenon so widely discussed, has never been formalized in the economics literature. The model of this paper accommodates well-known deviations from expected utility, such as the Allais paradox and the coexistence of gambling and insurance, while minimally deviating from expected utility.     

Introduction to the Special Issue in Honor of Peter Wakker

Theory and Decision -     

Firth adjusted score function for monotone likelihood in the mixture cure fraction model

Lifetime Data Analysis - Models for situations where some individuals are long-term survivors, immune or non-susceptible to the event of interest, are extensively studied in biomedical research....     

A Fuzzy Decision Tree for Fault Classification

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.     

Survival Weibull regression model for mismeasured outcomes

In some survival studies, the exact time of the event of interest is unknown, but the event is known to have occurred during a particular period of time (interval-censored data). If the diagnostic tool used to detect the event of interest is not perfectly sensitive and specific, outcomes may be mismeasured; a healthy subject may be diagnosed as sick and a sick one may be diagnosed as healthy. In such cases, traditional survival analysis methods produce biased estimates for the time-to-failure distribution parameters (Paggiaro and Torelli 2004 Paggiaro, A., and N. Torelli. 2004. The effect of classification errors in survival data analysis. Statistical Methods and Applications 13:213–25.[Crossref] , [Google Scholar]). In this context, we developed a parametric model that incorporates sensitivity and specificity into a grouped survival data analysis (a case of interval-censored data in which all subjects are tested at the same predetermined time points). Inferential aspects and properties of the methodology, such as the likelihood function and identifiability, are discussed in this article. Assuming known and non differential misclassification, Monte Carlo simulations showed that the proposed model performed well in the case of mismeasured outcomes; the estimates of the relative bias of the model were lower than those provided by the naive method that assumes perfect sensitivity and specificity. The proposed methodology is illustrated by a study related to mango tree lifetimes.     

Modelling the quality of work in the Italian social co-operatives combining NPCA-RSM and SEM-GME approaches

The objective of this paper is to describe and analyse with appropriate statistical models the links between work quality latent factors. Due to the complexity of the task, the analysis is carried out through a two-step approach:

• In the first step, we construct some multidimensional measures of the subjective quality of work, using nonlinear principal component analysis (NPCA) and Rasch analysis with the Rating Scale Model (NPCA-RSM);

• In the second step, we adopt a Structural Equation Model based on generalized maximum entropy (SEM-GME) to integrate the measures achieved with the previous step and to evaluate the relationships between the subjective work quality latent factors.

Therefore, the novel aspects of this paper are the following: (i) The integration between the NPCA-RSM and SEM-GME, which allows reduction of the variables analysed and evaluation of the measurement errors; (ii) The formalization of a Job Satisfaction Model for the study of the relationships between the subjective work quality latent factors in the Italian social services sector.     

Development and Governance in the Ex-Soviet Union: An Empirical Inquiry

The widespread adoption of the concept of wellbeing in the social sciences literature has allowed researchers to move beyond a narrow economic focus on income and consumption as the primary measure of inequality and poverty. Although statistical measurement and analyses of wellbeing have become increasingly feasible due to the availability of rich datasets, the empirical literature on wellbeing remains deficient in a number of ways. In this paper we argue that it is necessary that empirical studies of wellbeing encompass multiple. Applying Sen’s capability approach as our theoretical model we subsequently develop a modelling framework that applies the polychoric principal component analysis (PCA) method for the calculation of objective wellbeing and wellbeing inequality using household asset, education, health and housing dimensions. Findings from our case study of Vietnam challenge previous single dimensional analyses and reveal a number of layers to our understanding of wellbeing inequality in Vietnam. We argue that our use of polychoric PCA is especially suited to the analysis of wellbeing by explicitly incorporating the analysis of both cardinal and ordinal variables and overcoming deficiencies established in the literature relating to the use of standard PCA.