Contributions in Merlin

Continental Europe has seen a smaller rise in formal female employment compared with the United States or the Nordic countries. Additionally, Continental Europe has a substantially smaller service sector. These facts coincide with job requirements shifting from physical strength to intellectual capacity. Given empirical evidence, this paper develops a model of endogenous technical change, where new 'technologies' can be invented to increase the productivity of brain-inputs. Two inputs, brain and brawn, are combined through CES production functions into services and industrial goods, with the production sector for goods requiring more brawn than brain. Households allocate time to working at home or the labor market, choose consumption of services and goods, and invest in new technologies. The key is households can produce a substitute for market services and women have, on average, less brawn than men, giving them a comparative advantage with respect to staying home and working in the service sector. Therefore, an economy that does not facilitate the movement of women into the labor market, by imposing high taxes, causes service production to remain at home. This reduces technological innovation, pushing an economy into a self-reinforcing loop, where a small service sector feeds back into low total hours worked by women (and men), further depressing the service sector.

The aim of the present paper is to assess the effects of altered dopamine (DA) transmission on the functional connectivity among brain regions mediating aversive conditioning in humans. To this aim, we analyzed a previous published data set from a double-blind design combined with functional magnetic resonance imaging (fMRI) recordings in which healthy volunteers were randomly assigned to one of three drug groups: amphetamine (an indirect DA agonist), haloperidol (DA D2 receptor antagonist), and placebo. Participants were exposed to an aversive classical conditioning paradigm using cutaneous electrical stimulation as the unconditioned stimulus (US), and visual cues as the conditioned stimuli (CS) where one colour (CS+) was followed by the US in 33% of the trials and another colour (CS-) had no consequences. All participants reported awareness of stimulus contingencies. Group analysis of fMRI data revealed that the left ventral striatum (VS) and amygdala activated in response to the CS+ in all the three groups. Because of their activation patterns and documented involvement in aversive conditioning, both regions were used as seeds in the functional connectivity analysis. To constrain the functional networks obtained to relate to the conditioned response, we also correlated seed activity with the Galvanic Skin Response (GSR). In the placebo group, the right ventral tegmental area/substantia nigra (VTA/SN), bilateral caudate, right parahippocampal gyrus, left inferior parietal lobule (IPL), bilateral postcentral gyrus, bilateral middle frontal (BA 46), orbitofrontal, and ventromedial prefrontal cortices (PFC, BA 10/11) correlated with the VS and amygdala seeds in response to the CS+ compared to the CS-. Enhancing dopamine transmission via amphetamine was associated with reduced task differences and significant functional connectivity for both CS+ and CS- conditions between the left VS seed and regions modulated by DA, such as the left VTA/SN, right caudate, left amygdala, left middle frontal gyrus (BA 46), and bilateral ventromedial PFC (BA 10). Blocking dopamine transmission via haloperidol was associated with significant functional connectivity across an alternate network of regions including the left amygdala seed and the right insula, the left ACC (BA 24/32), bilateral IPL (BA 40), precuneus (BA 7), post-central gyrus, middle frontal gyrus (BA 46), and supplementary motor area (SMA, BA 6) to the CS+ versus the CS-. These data provide insight into the distinct effects of DA agents on the functional connectivity between striatal, limbic, and prefrontal areas

In this paper, we empirically evaluate competing approaches for combining inflation density forecasts in terms of Kullback–Leibler divergence. In particular, we apply a similar suite of models to four different datasets and aim at identifying combination methods that perform well throughout different series and variations of the model suite. We pool individual densities using linear and logarithmic combination methods. The suite consists of linear forecasting models with moving estimation windows to account for structural change. We find that combining densities is a much better strategy than selecting a particular model ex ante. While combinations do not always perform better than the best individual model, combinations always yield accurate forecasts and, as we show analytically, provide insurance against selecting inappropriate models. Logarithmic combinations can be advantageous, in particular if symmetric densities are preferred.

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