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The study examines the association between the size of previous environmental sanction charges and subsequent compliance towards environmental regulations. Data used for the study come from about 9000 Swedish firms fined sometime between January 2002 and December 2012. Probabilities of compliance across various levels of sanctions are estimated using life-table methods and tested for equality using standard nonparametric methods. Association between size of sanction charges and subsequent behaviour is modelled by proportional hazard model for the rate of recidivism as well as by a family of flexible parametric accelerated failure-time models for the duration of compliance. The results show that duration of compliance may be described by a log-normal distribution. Further, it is demonstrated that sanctions charges do have significant detering effects on the risk of recidivism though the strength of the detering effect depends on whether or not we account for other possible correlates of recidivism. Possible explanations of the results and their policy implications are discussed; limitations of the current study highlighted; and potential extensions for future studies outlined. [ABSTRACT FROM AUTHOR]

We explore effects of individual, family, and neighborhood effects on educational progress. The statistical model used is a multilevel sequential probit model. Such formulation allows the covariate-effects to vary across different educational levels. Results based on about 2,100 children whose parents are either native Swedes or migrants from Poland or Turkey provide new insights with regard to differentials in educational progress across background varaiables. Among others, we find that parental education is a strong predictor of educational progress at all levels. On the other hand, while family structure, family economy, and ethinic background are strongly correlated to educational progress at lower levels, their effect diminishes at higher levels of education. Possible exaplanations and implications of the results are presented. [ABSTRACT FROM AUTHOR]

Observations in Nigeria have indicated polio vaccination refusal related to religion that ultimately affected child morbidity and mortality. This study assessed the role of religion in under-five (0–59 months) mortality using a cross-sectional, nationally representative sample of 7,620 women aged 15–49 years from the 2003 Nigeria Demographic and Health Survey and included 6,029 children. Results show that mother’s affiliation to Traditional indigenous religion is significantly associated with increased under-five mortality. Multivariable modelling demonstrated that this association is explained by differential use of maternal and child health services, specifically attendance to prenatal care. To reduce child health inequity, these results need to be incorporated in the formulation of child health policies geared towards achieving a high degree of attendance to prenatal care, irrespective of religious affiliation. [ABSTRACT FROM AUTHOR]

We describe a flexible geo-additive Bayesian survival model that controls, simultaneously, for spatial dependence and possible nonlinear or time-varying effects of other variables. Inference is fully Bayesian and is based on recently developed Markov Chain Monte Carlo techniques. In illustrating the model we introduce a spatial dimension in modelling under-five mortality among Malawian children using data from Malawi Demographic and Health Survey of 2000. The results show that district-level socioeconomic characteristics are important determinants of childhood mortality. More importantly, a separate spatial process produces district clustering of childhood mortality indicating the importance of spatial effects. The visual nature of the maps presented in this paper highlights relationships that would, otherwise, be overlooked in standard methods. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between outcomes from separate hazard modeling of two competing risks and those based on hazard-modeling of the overall risk combined with logistic-regression on the conditional probability of the risk of interest. The formulation presented in this paper generializes for continuous time models and allows for non-categorical covariates. Our analytical results show that the two approaches are incompatible because they address different issues. Such a straightforward explanation of the incompatibility between the two approaches is expected to prompt investigators to focus on identifying situations of when the issue addressed by each approach may be of substantive interest. [ABSTRACT FROM AUTHOR]

The paper demonstrates how various parametric models for duration data such as the exponential, Weibull, and log-normal may be embedded in a single framework, and how such competing models may be assessed relative to a more comprehensive one. To illustrate the issues addressed, the survival patterns of marriages among 1203 Swedish men born 1936-1964 are studied by parametric and non-parametric survival methods. In particular, we study the sensitivity of model-choice with respect to level of aggregation of the time variable; and of covariate-effects with respect to the model chosen. In accordance with previous works our empirical results indicate that the choice of a parametric model for the duration variable is affected by the level of time aggregation. In contrast to previous results, however, our analysis shows that estimates of covariate effects are not always robust to distributional assumptions for the duration variable. [ABSTRACT FROM AUTHOR]

This paper reviews tests of equality between the sets of coefficients in the two linear regression models, and examines the effect of heteroscedasticity in each model on the behaviour of one such test. A Monte Carlo evaluation, of the size in particular, shows that the usual Chow's F-ratio is well behaved as long as the sample sizes in the two models are equal and the two models exhibit the same form of heteroscedasticity which is moderate in the context of the values used. In such situations it is recommended that the test be used directly to avoid the extra efforts involved in transforming the original data in order to attain homoscedasticity. [ABSTRACT FROM AUTHOR]

In this paper we examine a multiplicative intensity model in which a covariate interacts with two other covariates in the same model. We demonstrate, analytically, that in such situations a log-linear parameterization based on two pairs of baseline levels cannot be transformed, uniquely, to the, otherwise equivalent, multiplicative parameterization. We show that the problem lies in an oversight of the conditional independence between the two covariates interacting with a common third covariate. As a solution, therefore, we propose an approach that takes due account of such dependence. Our proposed approach uses a common baseline level for the three covariates involved in interaction while estimating the corresponding relative intensities. The issues addressed are illustrated with a demographic data set involving the estimation of rates of transition to parenthood. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to compare results of estimation and inference concerning covariate effects as obtained from two approaches to the analysis of survival data with multiple causes of failure. The first approach involves a dynamic model for the cause-specific hazard rate. The second is based on a static logistic regression model for the conditional probability of having had an event of interest. The influence of sociodemographic characteristics on the rate of family initiation and, more importantly, on the choice between marriage and cohabitation as a first union, is examined. We found that results, generally, are similar across the methods considered. Some issues in relation to censoring mechanisms and independence among causes of failure are discussed. [ABSTRACT FROM AUTHOR]