M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Does state ownership really matter? The dynamic alignment of China's resource environment and firm internationalization strategies

Recent research suggests that unequal access to home country institutional resources affects firm internationalization strategies. We add to this debate, based on an analysis of state-owned (SOEs) and non-state-owned (NSOEs) Chinese mining firms, by developing a more dynamic and multi-layered understanding of this interplay. We find that home institutional support can be ownership-based or performance-based, whereby the former benefits SOEs and the latter favors high-performing NSOEs. Combined, these support structures serve as institutional control mechanisms in promoting competition, performance and loyalty to national policies. In addition, they establish a permanent link between firm- and home country-specific assets and incentivize firms to develop dynamic resource access capabilities at home and abroad. Our findings inform our understanding of the firms-institutions nexus and Chinese foreign direct investment dynamics, especially in industries of strategic importance to the state.     

GENDER AND PROSOCIAL VALUES DURING ADOLESCENCE: A Research Note

To date, relatively little is known about the prosocial values of adolescents. Research has shown that females attach more importance to certain prosocial values than males do in late adolescence but has not considered whether this gender difference is evident across stages of adolescence and whether it varies by race. We examine values that focus on doing things for others, using data from a nationwide survey of 12- to 17-year-olds. We find that girls place more importance on these prosocial values than boys at younger ages. However, we also find important patterns in this gender difference across racial groups as well as across age. White boys appear to attach less importance to prosocial values than other race and gender groups. Furthermore, the gender gap in prosocial values is larger at older ages, with older adolescent boys placing less importance on prosocial values than younger ones.     

Implementing Fall Prevention Guidelines with Vulnerable Older Adults: The Social Work Role

Falls are the leading cause of injury among the older adult population, resulting in costly and devastating aftermaths. National fall prevention guidelines (FPGs) have been established to assist healthcare professionals with addressing fall risk, but little is understood about the extent to which FPGs have been implemented by social workers. Social workers, however, may beuniquely positioned to implement FPGs with older adults due to their expertise in care coordination and home- and community-based service settings. This conceptual paper addresses the timely issue of fall prevention and social workers’ potential role in implementing FPGs to address fall risk.     

Shape analysis for automated sulcal classification and parcellation of MRI data

We describe geometric invariants that characterize the shape of curves and surfaces in 3D space: curvature, Gauss integrals and moments. We apply these invariants to neuroimaging data to determine if they have application for automatically classifying and parcellating cortical data. The curves of sulci and gyri on the cortical surface can be obtained by reconstructing cortical surface representations of the human brain from magnetic resonance imaging (MRI) data. We reconstructed gray matter surfaces for 15 subjects, traced 10 sulcal curves on each surface and computed geometric invariants for each curve. These geometric features were used classify the curves into sulcal and hemispheric classes. The best classification results were obtained when moment-based features were computed on the sulcal curves in native space. Gauss integral measures showed that they were useful for differentiating the hemispheric location of a single sulcus. These promising results may indicate that moment invariants are useful for characterizing shape on a global scale. Gauss integral invariants are potentially useful measures for characterizing cortical shape on a local, rather than global scale. Gauss integrals have found biological significance in characterizing proteins so it is worthwhile to consider their possible application to neuroscientific data.     

Mixing Apples and Oranges and Other Methodological Problems with a Meta-Analysis of Long Term Psychodynamic Psychotherapy

Current trends in mental health practice indicate that mental health practitioners, including clinical social workers, are tending to move away from psychodynamic approaches and are favoring short-term empirically supported approaches such as cognitive-behavioral therapy (CBT). Recently, the presumption that long-term psychodynamic psychotherapy (LTPP) approaches lack empirical support has been challenged, however, with the publication in major peer reviewed journals of meta-analyses that are claimed to demonstrate its equality to or superiority over empirically supported short-term therapies for some conditions. The present article examines the methodology employed in one particular meta-analysis which compared LTPP to short term therapies and the studies upon which the analysis was based. We found that major methodological and statistical flaws with the meta-analysis and with the individual studies preclude drawing any conclusions regarding the efficacy of LTPP.     

Using the Delphi method to engage stakeholders: A comparison of two studies

Involving stakeholders can greatly impact evaluation results. The Delphi method, a consensus-building tool, is a promising process to promote and encourage involvement from all stakeholders during the evaluation framing process. The Delphi method removes geographic challenges and time boundaries allowing all stakeholders to participate. The Delphi method uses a series of surveys interspersed with controlled feedback designed to gather information and build consensus without requiring face-to-face meetings. Two different formats of the Delphi method, a paper-and-pencil, postal-mail version and a web-based, real-time computer version are compared in this study. Both versions of the Delphi were administered to a non-profit community based organization as part of framing an evaluation. Participation rates were better with the paper–pencil version. The quantity and quality of data collected were comparable in both versions.     

Bayesian modeling of temporal properties of infectious disease in a college student population

A Bayesian statistical model is developed for analysis of the time-evolving properties of infectious disease, with a particular focus on viruses. The model employs a latent semi-Markovian state process, and the state-transition statistics are driven by three terms: (i) a general time-evolving trend of the overall population, (ii) a semi-periodic term that accounts for effects caused by the days of the week, and (iii) a regression term that relates the probability of infection to covariates (here, specifically, to the Google Flu Trends data). Computations are performed using Markov Chain Monte Carlo sampling. Results are presented using a novel data set: daily self-reported symptom scores from hundreds of Duke University undergraduate students, collected over three academic years. The illnesses associated with these students are (imperfectly) labeled using real-time (RT) polymerase chain reaction (PCR) testing for several viruses, and gene-expression data were also analyzed. The statistical analysis is performed on the daily, self-reported symptom scores, and the RT PCR and gene-expression data are employed for analysis and interpretation of the model results.     

Local stationarity in small area estimation models

Small area estimators are often based on linear mixed models under the assumption that relationships among variables are stationary across the area of interest (Fay–Herriot models). This hypothesis is patently violated when the population is divided into heterogeneous latent subgroups. In this paper we propose a local Fay–Herriot model assisted by a Simulated Annealing algorithm to identify the latent subgroups of small areas. The value minimized through the Simulated Annealing algorithm is the sum of the estimated mean squared error (MSE) of the small area estimates. The technique is employed for small area estimates of erosion on agricultural land within the Rathbun Lake Watershed (IA, USA). The results are promising and show that introducing local stationarity in a small area model may lead to useful improvements in the performance of the estimators.