Dimensions of Social Stratification and Their Relation to Mortality: A Comparison Across Gender and Life Course Periods in Finland

Social Indicators Research - Differences in mortality between groups with different socioeconomic positions (SEP) are well-established, but the relative contribution of different SEP measures is...     

Comparing Observed and Unobserved Components of Childhood: Evidence From Finnish Register Data on Midlife Mortality From Siblings and Their Parents

In this study, we argue that the long arm of childhood that determines adult mortality should be thought of as comprising an observed part and its unobserved counterpart, reflecting the observed socioeconomic position of individuals and their parents and unobserved factors shared within a family. Our estimates of the observed and unobserved parts of the long arm of childhood are based on family-level variance in a survival analytic regression model, using siblings nested within families as the units of analysis. The study uses a sample of Finnish siblings born between 1936 and 1950 obtained from Finnish census data. Individuals are followed from ages 35 to 72. To explain familial influence on mortality, we use demographic background factors, the socioeconomic position of the parents, and the individuals’ own socioeconomic position at age 35 as predictors of all-cause and cause-specific mortality. The observed part—demographic and socioeconomic factors, including region; number of siblings; native language; parents’ education and occupation; and individuals’ income, occupation, tenancy status, and education—accounts for between 10 % and 25 % of the total familial influence on mortality. The larger part of the influence of the family on mortality is not explained by observed individual and parental socioeconomic position or demographic background and thus remains an unobserved component of the arm of childhood. This component highlights the need to investigate the influence of childhood circumstances on adult mortality in a comprehensive framework, including demographic, social, behavioral, and genetic information from the family of origin.     

Summary indices of the age distribution of a population

In this paper a method for characterizing the age distribution of a population by means of an ‘index word’ is outlined. It is shown that the method can be applied to yield approximations to population forecasts.     

Am I a leader? Examining leader identity development over time

The extent to which someone thinks of him- or herself as a leader (i.e., leader identity) is subject to change in a dynamic manner because of experience and structured intervention, but is rarely studied as such. In this study, we map the trajectories of leader identity development over a course of a seven-week leader development program. Drawing upon identity theory (Kegan, 1983) and self-perception theory (Bem, 1972), we propose that changes in self-perceived leadership skills are associated with changes in leader identity. Using latent growth curve modeling and latent change score analyses as our primary analytical approaches, we analyzed longitudinal data across seven measurement points (N = 98). We find leader identity to develop in a J-shaped pattern. As hypothesized, we find that these changes in leader identity are associated with, and potentially shaped by, changes in leadership skills across time.     

Gender differences in entrepreneurial intentions: A TPB multi-group analysis at factor and indicator level

Previous research suggests that diverse factors predict gender differences in entrepreneurial intent. Our paper integrates and expands on previous findings using the Theory of Planned Behavior (TPB), including the deeper-level measurement model, allowing for a better understanding of the origin of differences. The results of a survey with business students indicate that the effect of gender on entrepreneurial intentions is mediated via personal attitudes and perceived behavioral control but not social norms. More precisely, vis-à-vis their male counterparts, women are more driven toward entrepreneurship by motives to ‘get organized’ (balance) that are less dominant in predicting personal attitude. Moreover, female students are somewhat less driven toward entrepreneurship by beliefs of internal control that are more dominant in predicting perceived control. Finally, while female students are also more motivated to comply with normative role models, this did not influence their entrepreneurial intentions over and above perceived behavioral control and personal attitude. We discuss both practical and theoretical implications of our findings.     

Leadership and generations at work: A critical review

We present a critical review of theory, empirical research, and practical applications regarding generational differences in leadership phenomena. First, we consider the concept of generations both historically and through contemporary arguments related to leadership. Second, we outline and refute various myths surrounding the idea of generational differences in general, and critique leadership theories that have been influenced by these myths. Third, we describe the results of a literature review of primary empirical studies that have invoked the notion of generational differences to understand leadership phenomena. Finally, we argue that the lifespan developmental perspective represents a useful alternative to generational representations, as it better captures age-related dynamics that are relevant to leadership, followership, and leadership development. Ultimately, our work serves as a formal call for a moratorium to be placed upon the application of the ideas of generations and generational differences to leadership theory, research, and practice.     

A Bayesian approach to estimating linear mixtures with unknown covariance structure

In this paper, we study a new Bayesian approach for the analysis of linearly mixed structures. In particular, we consider the case of hyperspectral images, which have to be decomposed into a collection of distinct spectra, called endmembers, and a set of associated proportions for every pixel in the scene. This problem, often referred to as spectral unmixing, is usually considered on the basis of the linear mixing model (LMM). In unsupervised approaches, the endmember signatures have to be calculated by an endmember extraction algorithm, which generally relies on the supposition that there are pure (unmixed) pixels contained in the image. In practice, this assumption may not hold for highly mixed data and consequently the extracted endmember spectra differ from the true ones. A way out of this dilemma is to consider the problem under the normal compositional model (NCM). Contrary to the LMM, the NCM treats the endmembers as random Gaussian vectors and not as deterministic quantities. Existing Bayesian approaches for estimating the proportions under the NCM are restricted to the case that the covariance matrix of the Gaussian endmembers is a multiple of the identity matrix. The self-evident conclusion is that this model is not suitable when the variance differs from one spectral channel to the other, which is a common phenomenon in practice. In this paper, we first propose a Bayesian strategy for the estimation of the mixing proportions under the assumption of varying variances in the spectral bands. Then we generalize this model to handle the case of a completely unknown covariance structure. For both algorithms, we present Gibbs sampling strategies and compare their performance with other, state of the art, unmixing routines on synthetic as well as on real hyperspectral fluorescence spectroscopy data.     

A new bivariate exponential distribution for modeling moderately negative dependence

This paper introduces a new bivariate exponential distribution, called the Bivariate Affine-Linear Exponential distribution, to model moderately negative dependent data. The construction and characteristics of the proposed bivariate distribution are presented along with estimation procedures for the model parameters based on maximum likelihood and objective Bayesian analysis. We derive Jeffreys prior and discuss its frequentist properties based on a simulation study and MCMC sampling techniques. A real data set of mercury concentration in largemouth bass from Florida lakes is used to illustrate the methodology.