A Bayesian Analysis of Female Wage Dynamics Using Markov Chain Clustering

In this work, we analyze wage careers of women in Austria. We identify groups of female employees with similar patterns in their earnings development. Covariates such as e.g. the age of entry, the number of children or maternity leave help to detect these groups. We find three different types of female employees: (1) “high-wage mums”, women with high income and one or two children, (2) “low-wage mums”, women with low income and ‘many’ children and (3) “childless careers”, women who climb up the career ladder and do not have children. We use a Markov chain clustering approach to find groups in the discretevalued time series of income states. Additional covariates are included when modeling group membership via a multinomial logit model.Income Career, Transition Data, Multinomial Logit, Auxiliary Mixture Sampler, Markov Chain Monte Carlo

Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative data set. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model-based clustering approach. The statistical challenge in our application comes from the di±culty in extending distance-based clustering approaches to the problem of identify groups of similar time series in a panel of discrete-valued time series. We use Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter (2010), which is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model.Labor Market Entry Conditions, Transition Data, Markov Chain Monte Carlo, Multinomial Logit, Panel Data, Auxiliary Mixture Sampler, Bayesian Statistics

Bayesian Clustering of Categorical Time Series Using Finite Mixtures of Markov Chain Models

Two approaches for model-based clustering of categorical time series based on time- homogeneous first-order Markov chains are discussed. For Markov chain clustering the in- dividual transition probabilities are fixed to a group-specific transition matrix. In a new approach called Dirichlet multinomial clustering the rows of the individual transition matri- ces deviate from the group mean and follow a Dirichlet distribution with unknown group- specific hyperparameters. Estimation is carried out through Markov chain Monte Carlo. Various well-known clustering criteria are applied to select the number of groups. An appli- cation to a panel of Austrian wage mobility data leads to an interesting segmentation of the Austrian labor market.Markov chain Monte Carlo, model-based clustering, panel data, transition matrices, labor market, wage mobility

Bumblebee vision

Hummeln leben in unterschiedlichen Habitaten, ein Merkmal, das diese Habitate unterscheidet ist die spektrale Zusammensetzung des Habitats. Die größten Unterschiede in der spektralen Umgebung von Hummeln finden sich zwischen alpinen und Tiefland- Habitaten (Endler 1993). Anpassungen der spektralen Empfindlichkeit der Photorezeptoren an die photische Umwelt wurden bisher nicht gefunden (Peitsch et al 1992). In unserer Arbeit gingen wir der Frage nach, ob Anpassungen an unterschiedliche spektrale Bedingungen auf der Ebene der Genexpression zu finden sind. Zu diesem Zweck wurden Hummeln in unterschiedlichen spektralen Umgebungen gehalten und ihre Opsinexpression mittel Real-Time PCR gemessen. Weiters wurden Hummeln aus unterschiedlichen Höhenlagen gesammelt und die Opsinexpression wurde ebenfalls bestimmt. Wir konnten zeigen, daß das Sehsystem von Hummeln auf unterschiedliche spektrale Umgebungen auf der Ebene der Genexpression reagiert.Animals live in diverse habitats that vary in their light spectral quality. In terrestrial habitats the most extreme differences in spectral light quality occur between low and high altitudes, and between open habitats like grassland and dense forest (Endler 1993). However, most studies so far fail to show correlations between the spectral sensitivity of photoreceptors and the photic environment of the specific habitat, in spite of a distinct variation of spectral light quality among habitats of the species under comparison. Therefore, the possible mechanisms responsible for adaptation of terrestrial animals to their photic environment are still under debate.Bumblebees (Bombus) provide an ideal system to address these questions since they inhabit almost every terrestrial habitat and for many taxa the spectral sensitivity of their visual system is well characterized (Briscoe & Chittka 2001). Here we investigate whether bumblebees adapt to different photic environments by varying the relative level of visual pigments (opsins) in order to change the sensitivity of the respective photoreceptor types. The complex eye of bumblebees is composed of three different receptor types sensitive in the UV, blue and green part of the light spectrum (Briscoe & Chittka 2001). It has already been shown that the mRNA expression level of the long-wavelength (LWRh) opsin of Apis mellifera varies significantly over a 24 h cycle (Sasagawa et al 2003). We hypothesize that opsin expression levels correlate with the photic environments, e.g. bumblebees in alpine habitats, where UV radiation is high, express lower levels of UV opsin mRNA than bumblebees in lowland habitats or forests, where light spectrum is strongly shifted to longer wavelengths. Using real-time PCR, we first characterised the expression level of three opsin mRNAs (UV, blue and green) of Bombus terrestris individuals kept under controlled 12:12 hours L:D light regime over a 24 h period. We then entrained two experimental groups to short- and longwavelength shifted illumination, respectively, using color filters, and measured possible changes in the mRNA levels of the three opsins. Finally, we compared opsin expression levels of individuals of different Bombus species caught in the field from alpine habitats at altitudes > 2500 meters asl and from low land habitats

The effects of disturbance threat on leaf-cutting ant colonies: a laboratory study

The flexibility of organisms to respond plastically to their environment is fundamental to their fitness and evolutionary success. Social insects provide some of the most impressive examples of plasticity, with individuals exhibiting behavioural and sometimes morphological adaptations for their specific roles in the colony, such as large soldiers for nest defence. However, with the exception of the honey bee model organism, there has been little investigation of the nature and effects of environmental stimuli thought to instigate alternative phenotypes in social insects. Here we investigate the effect of repeated threat disturbance over a prolonged (17 month) period on both behavioural and morphological phenotypes, using phenotypically plastic leaf-cutting ants (Atta colombica) as a model system. We found a rapid impact of threat disturbance on the behavioural phenotype of individuals within threat-disturbed colonies becoming more aggressive, threat-responsive and phototactic within as little as two weeks. We found no effect of threat disturbance on morphological phenotypes, potentially because constraints such as resource limitation outweighed the benefit for colonies of producing larger individuals. The results suggest that plasticity in behavioural phenotypes can enable insect societies to respond to threats even when constraints prevent alteration of morphological phenotypes