Wages in high-tech start-ups - do academic spin-offs pay a wage premium?

Due to their origin from universities, academic spin‐offs operate at the forefront of the technological development. Therefore, spin‐offs exhibit a skill‐biased labour demand, i.e. spin‐offs have a high demand for employees with cutting edge knowledge and technical skills. In order to accommodate this demand, spin‐offs may have to pay a relative wage premium compared to other high‐tech start‐ups. However, neither a comprehensive theoretical assessment nor the empirical literature on wages in start‐ups unambiguously predicts the existence and the direction of wage differentials between spin‐offs and non‐spin‐offs. This paper addresses this research gap and examines empirically whether or not spin‐offs pay their employees a wage premium. Using a unique linked employer‐employee data set of German high‐tech start‐ups, we estimate Mincer‐type wage regressions applying the Hausman‐Taylor panel estimator. Our results show that spin‐offs do not pay a wage premium in general. However, a notable exception from this general result is that spin‐offs that commercialise new scientific results or methods provide higher wages to employees with linkages to the university sector – either as university graduates or as student workers

RE: pedagogy – after neutrality

Within the UK and in many parts of the world, official accounts of what it is to make sense of religion are framed within a rhetorics of neutrality in which such study is premised upon the possibility of dispassionate engagement and analysis. This paper, which is largely theoretical in scope, explores both the affordances and the costs of such an approach which has become ‘black boxed’ on account of the work that it achieves. A series of new orientations within the academy that are broadly associated with post-structuralist philosophies, feminist and post-colonial studies, together with insights from Science and Technology Studies, question the plausibility of these claims for neutrality whilst in turn raising a series of new questions and priorities. It therefore becomes necessary to re-think and re-frame what it is to make sense of religious and cultural difference after neutrality. The gathering and co-ordination of new planes of sense-making that are responsive to an emergent series of epistemological, ontological, and ethical orientations are considered. Some of the distinctive pedagogical implications of such an approach that engages material practice, difference and uncertainty are then entertained

A genome scan for milk production traits in dairy goats reveals two new mutations in <i>Dgat1</i> reducing milk fat content

The quantity of milk and milk fat and proteins are particularly important traits in dairy livestock. However, little is known about the regions of the genome that influence these traits in goats. We conducted a genome wide association study in French goats and identified 109 regions associated with dairy traits. For a major region on chromosome 14 closely associated with fat content, the Diacylglycerol O-Acyltransferase 1 (DGAT1) gene turned out to be a functional and positional candidate gene. The caprine reference sequence of this gene was completed and 29 polymorphisms were found in the gene sequence, including two novel exonic mutations: R251L and R396W, leading to substitutions in the protein sequence. The R251L mutation was found in the Saanen breed at a frequency of 3.5% and the R396W mutation both in the Saanen and Alpine breeds at a frequencies of 13% and 7% respectively. The R396W mutation explained 46% of the genetic variance of the trait, and the R251L mutation 6%. Both mutations were associated with a notable decrease in milk fat content. Their causality was then demonstrated by a functional test. These results provide new knowledge on the genetic basis of milk synthesis and will help improve the management of the French dairy goat breeding program

Lifetime feed efficiency and deep phenotypes from scarce feed intake records using the mechanistic LiGAPS-Dairy model

Ideally, selection for feed efficiency requires deep phenotyping of net efficiency, or lifetime recording of intake and all energy sinks across environments. However, recording of feed intake is scarce. Therefore, net efficiency is often defined as a simplistic linear equation, e.g. RFI. We tested the use of the mechanistic LiGAPS-Dairy model to derive nine deep phenotypes with a dataset for 1,228 dairy cows, combining feed intake, yield and liveweight data, with ration, weather, cow and farm data. Mismatch between data recording and model assumptions made this process time consuming, but allowing for missing parities and further automation should improve this quickly. We managed for 206 cows to estimate the deep phenotypes. Heritability and phenotypic correlations between the nine traits were estimated. When the pipeline is finished, the mechanistic LiGAPS-Dairy model will enable us to derive a more comprehensive breeding goal, more closely resembling net efficiency, whilst utilising scarce records