Towards precision distances and 3D dust maps using broadband Period--Magnitude relations of RR Lyrae stars

We determine the period-magnitude relations of RR Lyrae stars in 13 photometric bandpasses from 0.4 to 12 {\mu}m using timeseries observations of 134 stars. The Bayesian formalism, extended from our previous work to include the effects of line-of-sight dust extinction, allows for the simultaneous inference of the posterior distribution of the mean absolute magnitude, slope of the period-magnitude power-law, and intrinsic scatter about a perfect power-law for each bandpass. In addition, the distance modulus and line-of-sight dust extinction to each RR Lyrae star in the calibration sample is determined, yielding a sample median fractional distance error of 0.66%. The intrinsic scatter in all bands appears to be larger than the photometric errors, except in WISE W1 (3.4 {\mu}m) and W2 (4.6 {\mu}m) where the photometric error ($\sigma \approx 0.05$ mag) is to be comparable or larger than the intrinsic scatter. Additional observations at these wavelengths could improve the inferred distances to these sources further. As an application of the methodology, we infer the distance to the RRc-type star RZCep at low Galactic latitude ($b = 5.5^\circ$) to be $\mu=8.0397\pm0.0123$ mag ($405.4\pm2.3$ pc) with colour excess $E(B-V)=0.2461\pm0.0089$ mag. This distance, equivalent to a parallax of $2467\pm14$ microarcsec, is consistent with the published HST parallax measurement but with an uncertainty that is 13 times smaller than the HST measurement. If our measurements (and methodology) hold up to scrutiny, the distances to these stars have been determined to an accuracy comparable to those expected with Gaia. As RR Lyrae are one of the primary components of the cosmic distance ladder, the achievement of sub-1% distance errors within a formalism that accounts for dust extinction may be considered a strong buttressing of the path to eventual 1% uncertainties in Hubble's constant.Comment: 21 pages, 29 figures, 2 tables, abstract abridged for arXiv. Comments solicited on referee report (received June 9, 2014) linked: https://gist.github.com/profjsb/c6c4e2f3a20ea02f1762 . Public archive of code used to generate results and figures: https://github.com/ckleinastro/period_luminosity_relation_fittin

Issues in the design and implentation of an R&D tax credit for the UK

R&D tax credits have become a popular policy tool for encouraging research and development (R&D) spending by business, with many countries offering subsidies of this form. The divergence between private and social rates of return to R&D expenditure by private firms provides one of the main justifications for government subsidies to R&D.2 In order to achieve the optimal level of R&D investment, government policy aims to bring private incentives in line with the social rate of return. An R&D tax credit does this by reducing the cost to the firm of doing R&D. Recent empirical evidence suggests that R&D tax credits are an effective instrument in stimulating additional R&D. However, in order to be desirable, a policy needs to be cost-effective and implementable. This Briefing Note reviews some of the major issues in the design and implementation of R&D tax credits. In Section 2, we briefly discuss the existing tax treatment of R&D in the UK. In particular, we outline the new Research and Development Allowance - which is an allowance for expenditure on plant, machinery and buildings for use in scientific research and which is available to firms of all sizes - and the tax credit for R&D that is available to small and medium-sized enterprises (SMEs). We then discuss, in Section 3, some of the main design features of tax credits that have been implemented in other countries. The discussion mainly concerns the question of how to target new or incremental R&D so as to keep down the total exchequer cost. We discuss problems that arise in defining incremental R&D and how these can be tackled. In Section 4, we provide estimates of the amount of new R&D and the exchequer cost that would be likely to result from implementing different designs of R&D tax credit in the UK. Section 5 concludes. Some technical details are dealt with in the Appendix

Competition and innovation: an inverted U relationship

This paper investigates the relationship between product market competition (PMC) and innovation. A Schumpeterian growth model is developed in which firms innovate ‘step-by-step’, and where both technological leaders and their followers engage in R&D activities. In this model, competition may increase the incremental profit from innovating; on the other hand, competition may also reduce innovation incentives for laggards. This model generates four main predictions which we test empirically. First, the relationship between product market competition (PMC) and innovation is an inverted U-shape: the escape competition effect dominates for low initial levels of competition, whereas the Schumpeterian effect dominates at higher levels of competition. Second, the equilibrium degree of technological ‘neck-and-neckness’ among firms should decrease with PMC. Third, the higher the average degree of ‘neck-and-neckness’ in an industry, the steeper the inverted-U relationship between PMC and innovation in that industry. Fourth, firms may innovate more if subject to higher debt-pressure, especially at lower levels of PMC. We confront these four predictions with a new panel data set on UK firms’ patenting activity at the US patenting office. The inverted U relationship, the neck and neck, and the debt pressure predictions are found to accord well with observed behavior in the data

Competition and innovation: an inverted U relationship?

This paper investigates the relationship between product market competition and innovation. It uses the radical policy reforms in the UK as instruments for changes in product market competition, and finds a robust inverted-U relationship between competition and patenting. It then develops an endogenous growth model with step-by-step innovation that can deliver this inverted-U pattern. In this model, competition has an ambiguous effect on innovation. On the one hand, it discourages laggard firms from innovating, as it reduces their rents from catching up with the leaders in the same industry. On the other hand, it encourages neck-and-neck firms to innovate in order to escape competition with their rival. The inverted-U pattern results from the interplay between these two effects, together with the effect of competition on the equilibrium industry structure. The model generates two additional predictions: on the relationship between competition and the average technological distance between leaders and followers across industries; and on the relationship between the distance of an industry to its technological frontier and the steepness of the inverted-U. Both predictions are supported by the data

Policy uncertainty: a new indicator

The damaging impact of economic uncertainty on growth has been reasonably well studied - but what happens when there is uncertainty about economic policy-making? Nicholas Bloom and colleagues have developed a measure of this distinct kind of uncertainty, one that shows the value of restoring stability to current policy actions.US economic policy, global financial markets,