A comparison of approaches for estimating the effect of women's education on the probability of using modern contraceptive methods in Malawi

The aim of this study is to estimate the effect of education on the probability of married Malawian women using modern contraceptive methods by accounting for both observed and unobserved confounders. We conduct a sensitivity analysis and compare the results of naive models with instrumental variable models to account for the potential endogeneity of education. Our findings demonstrate conflicting results between the two modelling approaches. The naive models report smaller education effects on the probability of using modern contraceptive methods compared to instrumental variable models. We also find that by relaxing the functional form assumption on the effect of continuous covariates, using a flexible instrumental variable model, the education's effect follows a positive, nonlinear pattern. This finding is not observed with a classic instrumental variable model.     

Crisis communication plans: Poor predictors of excellent crisis public relations

Public relations is an important element in almost all successful crisis mana ment efforts. Newly developed theory in crisis public relations, though, suggests a shift is necessary in the way practitioners view crises. The well-accepted tactics and techniques advocated by practitioners need to be replaced with strategies and beliefs from a new perspective—a perspective or paradigm that defines excellent crisis public relations practice very differently from the literature of the past 20 years.     

Regression spline bivariate probit models: A practical approach to testing for exogeneity

Bivariate probit models can deal with a problem usually known as endogeneity. This issue is likely to arise in observational studies when confounders are unobserved. We are concerned with testing the hypothesis of exogeneity (or absence of endogeneity) when using regression spline recursive and sample selection bivariate probit models. Likelihood ratio and gradient tests are discussed in this context and their empirical properties investigated and compared with those of the Lagrange multiplier and Wald tests through a Monte Carlo study. The tests are illustrated using two datasets in which the hypothesis of exogeneity needs to be tested.     

Semi-parametric bivariate polychotomous ordinal regression

A pair of polychotomous random variables \((Y_1,Y_2)^\top =:{\varvec{Y}}\), where each \(Y_j\) has a totally ordered support, is studied within a penalized generalized linear model framework. We deal with a triangular generating process for \({\varvec{Y}}\), a structure that has been employed in the literature to control for the presence of residual confounding. Differently from previous works, however, the proposed model allows for a semi-parametric estimation of the covariate-response relationships. In this way, the risk of model mis-specification stemming from the imposition of fixed-order polynomial functional forms is also reduced. The proposed estimation methods and related inferential results are finally applied to study the effect of education on alcohol consumption among young adults in the UK.     

Copula regression spline models for binary outcomes

We introduce a framework for estimating the effect that a binary treatment has on a binary outcome in the presence of unobserved confounding. The methodology is applied to a case study which uses data from the Medical Expenditure Panel Survey and whose aim is to estimate the effect of private health insurance on health care utilization. Unobserved confounding arises when variables which are associated with both treatment and outcome are not available (in economics this issue is known as endogeneity). Also, treatment and outcome may exhibit a dependence which cannot be modeled using a linear measure of association, and observed confounders may have a non-linear impact on the treatment and outcome variables. The problem of unobserved confounding is addressed using a two-equation structural latent variable framework, where one equation essentially describes a binary outcome as a function of a binary treatment whereas the other equation determines whether the treatment is received. Non-linear dependence between treatment and outcome is dealt using copula functions, whereas covariate-response relationships are flexibly modeled using a spline approach. Related model fitting and inferential procedures are developed, and asymptotic arguments presented.     

Mortality due to External Causes in Three Rural Areas of Senegal

Mortality due to external causes was measured over the period 1985–2004 in three rural areas of Senegal—Bandafassi, Niakhar and Mlomp – whose populations have been under continuous demographic surveillance for many years. The standardized annual rate of deaths due to external causes is 31 deaths per 100,000 inhabitants in Niakhar, 56 in Bandafassi and 102 in Mlomp. The causes of injury-related deaths generally reflect the rural living environment, with relatively few deaths due to road accidents (1.9 deaths per 100,000 inhabitants in Niakhar, 3.0 in Bandafassi and 2.0 in Mlomp), but many deaths due to falls (8.6 deaths per 100,000 inhabitants in Niakhar, 15.1 in Bandafassi and 23.3 in Mlomp). For certain causes, mortality varies considerably. Snake bites, for example, cause 0.1 deaths per 100,000 inhabitants in Niakhar, 13.4 in Bandafassi and 3.0 in Mlomp. The differences between sites are linked in this case to the relative concentrations of wildlife, in turn linked to differences in the local environment and in population densities (144 inhabitants per sq.km in Niakhar versus 19 in Bandafassi and 114 in Mlomp). Although the study areas are still largely unaffected by causes of death associated with development, such as traffic accidents, mortality due to external causes is high.     

Generalised joint regression for count data: a penalty extension for competitive settings

Statistics and Computing - We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and...     

Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity

The classic recursive bivariate probit model is of particular interest to researchers since it allows for the estimation of the treatment effect that a binary endogenous variable has on a binary outcome in the presence of unobservables. In this article, the authors consider the semiparametric version of this model and introduce a model fitting procedure which permits to estimate reliably the parameters of a system of two binary outcomes with a binary endogenous regressor and smooth functions of continuous covariates. They illustrate the empirical validity of the proposal through an extensive simulation study. The approach is applied to data from a survey, conducted in Botswana, on the impact of education on women's fertility. Some studies suggest that the estimated effect could have been biased by the possible endogeneity arising because unobservable confounders (e.g., ability and motivation) are associated with both fertility and education. The Canadian Journal of Statistics 39: 259–279; 2011 © 2011 Statistical Society of Canada     

Social learning about levels of perinatal and infant mortality in Niakhar,Senegal

How individuals develop perceptions concerning the risk of infant and child mortality has important consequences for fertility and demographic transition theory and for understanding broader processes of social learning. The role of learning through social interaction in shaping demographic phenomena has been the subject of intense research in the last decade. Much previous research however has been hampered by inadequate measures of individuals’ personal networks, the proximal context in which learning takes place. Using pilot data employing an innovative social network design in conjunction with demographic surveillance data from Niakhar, Senegal, this research models perception of change in the level of infant mortality over time as a function of the experience of social network associates with perinatal and infant mortality. Results suggest relatively strong effects of network members’ mortality experience controlling for own experiences of child mortality as well as neighborhood and community levels of infant mortality among other controls.