An R&D-Based Model of Multi-Sector Growth

We develop a multi-sector general equilibrium model in which productivity growth is driven by the production of sector-specific knowledge. In the model, we find that long run differences in total factor productivity growth across sectors are independent of the parameters of the knowledge production function except for one, which we term the fertility of knowledge. Differences in R&D intensity are also independent of most other parameters. The fertility of knowledge in the capital sector is central to the growth properties of the model economy.Endogenous technical change, multisector growth, fertility of knowledge, total factor productivity, R&D intensity, investment-specific technical change

Mapping Prices into Productivity in Multisector Growth Models

Two issues related to mapping a multi-sector model into a reduced-form value-added model are often neglected: the composition of intermediate goods, and the distinction between value added productivity and gross output productivity. We demonstrate their quantitative significance for the case of the well known model of Greenwood, Hercowitz and Krusell (1997), who find that about 60% of economic growth can be attributed to investment-specific technical change (ISTC). When we recalibrate their model to allow for even a small equipment share of intermediates, we find that ISTC accounts for almost the entirety of postwar US growth.Intermediate goods, investment-specific technical change, growth accounting, gross output, multisector growth models

Accounting for Research and Productivity Growth Across Industries

What factors underlie industry differences in research intensity and productivity growth? We develop a multi-sector endogenous growth model allowing for industry specific parameters in the production functions for output and knowledge, and in consumer preferences. We find that industry differences in both productivity growth and R&D intensity mainly reflect differences in "technological opportunities", interpreted as parameters of knowledge production. These include the capital intensity of R&D, knowledge spillovers, and diminishing returns to R&D. Among these parameters, we find that the degree of diminishing returns to R&D is the dominant factor when the model is calibrated to account for crossindustry differences in the US.Multisector growth, total factor productivity, R&D intensity, technological opportunity

Two intracellular and cell type-specific bacterial symbionts in the placozoan Trichoplax H2

Placozoa is an enigmatic phylum of simple, microscopic, marine metazoans(1,2). Although intracellular bacteria have been found in all members of this phylum, almost nothing is known about their identity, location and interactions with their host(3-6). We used metagenomic and metatranscriptomic sequencing of single host individuals, plus metaproteomic and imaging analyses, to show that the placozoan Trichoplax sp. H2 lives in symbiosis with two intracellular bacteria. One symbiont forms an undescribed genus in the Midichloriaceae (Rickettsiales)(7,8) and has a genomic repertoire similar to that of rickettsial parasites(9,10), but does not seem to express key genes for energy parasitism. Correlative image analyses and three-dimensional electron tomography revealed that this symbiont resides in the rough endoplasmic reticulum of its host's internal fibre cells. The second symbiont belongs to the Margulisbacteria, a phylum without cultured representatives and not known to form intracellular associations(11-13). This symbiont lives in the ventral epithelial cells of Trichoplax, probably metabolizes algal lipids digested by its host and has the capacity to supplement the placozoan's nutrition. Our study shows that one of the simplest animals has evolved highly specific and intimate associations with symbiotic, intracellular bacteria and highlights that symbioses can provide access to otherwise elusive microbial dark matter

Gaussian density fluctuations, mode coupling theory, and all that

We consider a toy model for glassy dynamics of colloidal suspensions: a single Brownian particle diffusing among immobile obstacles. If Gaussian factorization of static density fluctuations is assumed, this model can be solved without factorization approximation for any dynamic correlation function. The solution differs from that obtained from the ideal mode coupling theory (MCT). The latter is equivalent to including only some, positive definite terms in an expression for the memory function. An approximate re-summation of the complete expression suggests that, under the assumption of Gaussian factorization of static fluctuations, mobile particle's motion is always diffusive. In contrast, MCT predicts that the mobile particle becomes localized at a high enough obstacle density. We discuss the implications of these results for models for glassy dynamics.Comment: to be published in Europhys. Let