No Man is an Island: the Inter-personal Determinants of Regional Well-Being in Europe

There is a strong need to complement the analysis of social well-being at the European regional level to supplement existing, predominantly economic analysis. This work extends the measurement of well-being across the EU-15 regions in several ways. First, we assess the determinants of well-being using a multilevel modelling approach using data at the national, regional and individual levels. Second, we have extended the model to account for the effects of social interactions within each group, as well as intrinsic socio-demographic indicators and higher-level exogenous contextual factors. Empirical findings support the idea that well-being is strongly dependent both on these general forms of social interactions and on more specific individual characteristics. We find that there is some evidence of greater regional effects relative to national effects, but individual well-being continues to be affected most by micro-level phenomena

Mental health problems in urban neighbourhoods: the role of physical and social environment

Humans are surrounded by the outer environments and keep interacting with them. Humans face the challenge of the external world throughout their life course, adapt to the change, and strive to thrive. However, some environmental aspects deteriorate human health and leave sequelae throughout human life. This thesis uses a theoretical framework, Neighbourhood Mental Health Map, to describe the human-neighbourhood relationship in an ecological way. In line with this framework, the present thesis presents three empirical studies disentangling the complex interactions between human mental health and the physical and social environment in modern urban neighbourhoods. The first two studies were dedicated to the relationship between physical environments and mental health problems, and the third study focused on the social environment and development of adolescent mental health. In more detail, the first study aimed to identify the syndemic structure of mental problems in the context of urban neighbourhood environments characterized by high nighttime light exposure. The second study sought to identify the physical signatures (i.e., specific geographic patterns) of different mental health problems (e.g., depression, overdrinking). The third study tried to identify the roles of social mechanisms (e.g., social cohesion, informal social control and deviant peer affiliation) and conduct disorder development at varying levels of deprivation

Person-Centered Treatment to Optimize Psychiatric Medication Adherence

Objectives: Adherence to psychotropic medication is poor among individuals with bipolar disorder (BD). To understand treatment experiences and associated adherence among these individuals, we developed a novel construct of Clinical Net Benefit (CNB) using psychiatric symptoms, adverse effects and overall functioning assessments. We tested whether adherence differed across classes of CNB, whether individuals transitioned between classes over time, and whether these transitions were differentially associated with adherence. Methods: Data come from individuals aged 18+ during five years of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Latent class analysis identified groups of CNB. Latent transition analysis determined probabilities of transitioning between classes over time. Adherence was defined as taking 75%+ of medications as prescribed. Associations between CNB and adherence were tested using multiple logistic regression adjusting for sociodemographic characteristics. Results: Five classes of CNB were identified during the first two years (high, moderately high, moderate, moderately low, low), and four classes (removing moderately high) during the last three years. Adherence did not differ across classes or time points. Medication regimens differed by class; those with higher CNB taking fewer medications had lower odds of adherence while those with lower CNB taking more medications had higher odds of adherence compared with monotherapy. Probability of transitioning from higher to lower CNB, and lower to higher CNB was greatest over time. Conclusions: CNB is heterogeneous in individuals treated for BD, and movement between classes is not uncommon. Understanding why individuals adhere despite suboptimal CNB may provide novel insights into aspects influencing adherence

Labor Supply with Social Interactions: Econometric Estimates and Their Tax Policy Implications

Our research fleshes out econometric details of examining possible social interactions in labor supply. We look for a response of a person\u27s hours worked to hours worked in the labor market reference group, which includes those with similar age, family structure, and location. We identify endogenous spillovers by instrumenting average hours worked in the reference group with hours worked in neighboring reference groups. Estimates of the canonical labor supply model indicate positive economically important spillovers for adult men. The estimated total wage elasticity of labor supply is 0.22, where 0.08 is the exogenous wage change effect and 0.14 is the social interactions effect. We demonstrate how ignoring or incorrectly considering social interactions can mis-estimate the labor supply response of tax reform by as much as 60 percent

Labor Supply with Social Interactions: Econometric Estimates and Their Tax Policy Implications

Our research fleshes out econometric details of examining possible social interactions in labor supply. We look for a response of a person\u27s hours worked to hours worked in the labor market reference group, which includes those with similar age, family structure, and location. We identify endogenous spillovers by instrumenting average hours worked in the reference group with hours worked in neighboring reference groups. Estimates of the canonical labor supply model indicate positive economically important spillovers for adult men. The estimated total wage elasticity of labor supply is 0.22, where 0.08 is the exogenous wage change effect and 0.14 is the social interactions effect. We demonstrate how ignoring or incorrectly considering social interactions can mis-estimate the labor supply response of tax reform by as much as 60 percent

Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues

We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually expressed as fractions of the confusion matrix, such as sensitivity, specificity, negative and positive predictive values. We extend this concept to soft classification and discuss the bias and variance properties of the extended performance measures. Ambiguity in reference labels translates to differences between best-case, expected and worst-case performance. We show a second set of measures comparing expected and ideal performance which is closely related to regression performance, namely the root mean squared error RMSE and the mean absolute error MAE. All calculations apply to classical crisp classification as well as to soft classification (partial class memberships and/or one-class classifiers). The proposed performance measures allow to test classifiers with actual borderline cases. In addition, hardening of e.g. posterior probabilities into class labels is not necessary, avoiding the corresponding information loss and increase in variance. We implement the proposed performance measures in the R package "softclassval", which is available from CRAN and at http://softclassval.r-forge.r-project.org. Our reasoning as well as the importance of partial memberships for chemometric classification is illustrated by a real-word application: astrocytoma brain tumor tissue grading (80 patients, 37000 spectra) for finding surgical excision borders. As borderline cases are the actual target of the analytical technique, samples which are diagnosed to be borderline cases must be included in the validation.Comment: The manuscript is accepted for publication in Chemometrics and Intelligent Laboratory Systems. Supplementary figures and tables are at the end of the pd

Mapping geographical differences and examining the determinants of childhood stunting in Ethiopia : a Bayesian geostatistical analysis

Understanding the specific geographical distribution of stunting is essential for planning and implementing targeted public health interventions in high-burdened countries. This study investigated geographical variations in the prevalence of stunting sub-nationally, and the determinants of stunting among children under 5 years of age in Ethiopia. We used the 2016 Ethiopia Demographic and Health Survey (EDHS) dataset for children aged 0–59 months with valid anthropometric measurements and geographic coordinates (n = 9089). We modelled the prevalence of stunting and its determinants using Bayesian geospatially explicit regression models. The prevalence of stunting among children under five years was 36.3% (95% credible interval (CrI); 22.6%, 51.4%) in Ethiopia, with wide variations sub-nationally and by age group. The prevalence of childhood stunting ranged from 56.6% (37.4–74.6%) in the Mekelle Special zone of the Tigray region to 25.5% (10.5–48.9%) in the Sheka zone of the Southern Nations, Nationalities and Peoples region. Factors associated with a reduced likelihood of stunting in Ethiopia included non-receipt of breastmilk, mother’s BMI (overweight/obese), employment status (employed), and higher household wealth, while the enablers were residence in the “arid” geographic areas, small birth size of the child, and mother’s BMI (underweight). The prevalence and determinants of stunting varied across Ethiopia. Efforts to reduce the burden of childhood stunting should consider geographical heterogeneity and modifiable risk factors

Social Interactions in the Labor Market

We examine theoretically and empirically social interactions in labor markets and how policy prescriptions can change dramatically when there are social interactions present. Spillover effects increase labor supply and conformity effects make labor supply perfectly inelastic at a reference group average. The demand for a good may also be influenced by either a spillover effect or a conformity effect. Positive spillover increases the demand for the good with interactions, and a conformity effect makes the demand curve pivot to become less price sensitive. Similar social interactions effects appear in the associated derived demands for labor. Individual and community factors may influence the average length of poverty spells. We measure local economic conditions by the county unemployment rate and neighborhood spillover effects by the racial makeup and poverty rate of the county. We find that moving an individual from one standard deviation above the mean poverty rate to one standard deviation below the mean poverty rate (from the inner city to the suburbs) lowers the average poverty spell by 20–25 percent. We further consider overall labor market outcomes by examining theoretically the socially optimal wealth distribution. Interdependence in utility can mitigate the need to transfer wealth to low-wage individuals and may require them to be poorer by all objective measures. Finally, we quantify how labor market policy changes when there are household social interactions. Labor supply estimates indicate positive economically important spillovers for adult U.S. men. Ignoring or incorrectly considering social interactions can mis-estimate the labor supply response of tax reform in the United States by as much as 60 percent.social interactions, spillover, conformity, inequality, poverty, labor supply, reference group, social multiplier, income tax, PSID

Farmers' willingness to accept payments for ecosystem services on agricultural land:The case of climate-smart agroforestry in Ethiopia

This study examines smallholder farmers' preferences for the uptake of contractual climate-smart agroforestry, which yields economic and ecosystem benefits. A discrete choice experiment was conducted with smallholder farmers in Ethiopia to elicit their willingness to participate in a payment for ecosystem services (PES) scheme that incentivizes integrating faidherbia albida (a fertilizer tree) in their mono-cropping farming system. Attributes evaluated are "number of planted trees", "payment amount", "payment type", and "contract period". The presence of heterogeneity in the choice behavior of farmers warrants the use of the generalized multinomial logit and latent class conditional logit models to allow for farmer-and class-specific preferences, respectively. The results show that farmers derive higher utility from up-front payments. Farmers also strongly prefer food as the mode of payment than cash