Agglomeration Elasticities in New Zealand

This paper analyses the relationship between firms’ multi-factor productivity and the effective employment density of the areas where they operate. Quantifying these agglomeration elasticities is of central importance in the evaluation of the wider economic benefits of transport investments. We estimate agglomeration elasticities using the Statistics New Zealand prototype Longitudinal Business Database: a firm-level panel covering the period 1999 to 2006. We estimate that an area with 10 percent higher effective density has firms with productivity that is 0.69 percent higher, once we control for the industry specific production functions and sorting of more productive firms across industries and locations. We present separate estimates of agglomeration elasticities for specific industries and regions, and examine the interaction of agglomeration with capital, labour, and other inputs.Agglomeration, urban density, transport evaluation, productivity

Anabolic resistance does not explain sarcopenia in patients with type 2 diabetes mellitus, compared with healthy controls, despite reduced mTOR pathway activity

BackgroundAgeing and type 2 diabetes mellitus (T2DM) are risk factors for skeletal muscle loss. We investigated whether anabolic resistance to feeding might underlie accelerated muscle loss in older people with T2DM and whether dysregulated mTOR signalling was implicated.Subjects8 obese men with T2DM, and 12 age-matched controls were studied (age 68±3 vs. 68±6y; BMI: 30±2 vs. 27±5 kg·m-2).MethodsBody composition was measured by dual-X-ray absorptiometry. Insulin and glucose were clamped at post-absorptive concentrations (13±2 vs. 9±3 mU·l-1; 7.4±1.9 vs. 4.6±0.4 mmol·l-1; T2DM vs. controls). Fractional synthetic rates (FSR) of myofibrillar and sarcoplasmic proteins were measured as the rate of incorporation of [13C] leucine during a primed, constant infusion of [1-13C] α-ketoisocaproic acid, 3 h after 10 or 20g of essential amino acids (EAA) were orally administered. Protein expression of total and phosphorylated mTOR signalling proteins was determined by Western blot analysis.ResultsDespite a significantly lower appendicular lean mass index and a greater fat mass index in T2DM vs. controls, basal myofibrillar and sarcoplasmic and post-prandial myofibrillar FSR were similar. After 20g EAA, stimulation of sarcoplasmic FSR was slightly blunted in T2DM patients. Furthermore, feeding 20g EAA increased phosphorylation of mTOR, p70S6k and 4E-BP1 by 60-100% in controls with no response observed in T2DM.ConclusionsThere was clear dissociation between changes in mTOR signalling versus changes in protein synthesis rates. However, the intact anabolic response of myofibrillar FSR to feeding in both groups suggests anabolic resistance may not explain accelerated muscle loss in T2DM

Comparing the information capacity of entangled Laguerre-Gaussian and Hermite-Gaussian modal sets in a finite-aperture system

Using a spontaneous parametric down-conversion process to create entangled spatial states, we compare the information capacity associated with measurements in the Hermite–Gaussian and Laguerre–Gaussian modal basis in an optical system of finite aperture. We show that the cross-talk imposed by the aperture restriction degrades the information capacity. However, the Laguerre–Gaussian mode measurements show greater resilience to cross talk than the Hermite–Gaussian, suggesting that the Laguerre–Gaussian modal set may still offer real-world advantages over other modal sets

Integrating holism and reductionism in the science of art perception

The contextualist claim that universalism is irrelevant to the proper study of art can be evaluated by examining an analogous question in neuroscience. Taking the reductionist-holist debate in visual neuroscience as a model, we see that the analog of orthodox contextualism is untenable, whereas integrated approaches have proven highly effective. Given the connection between art and vision, unified approaches are likewise more germane to the scientific study of ar

Causal inference for data centric engineering

The paper reviews methods that seek to draw causal inference from observational data and demonstrates how they can be applied to empirical problems in engineering research. It presents a framework for causal identification based on the concept of potential outcomes and reviews core contemporary methods that can be used to estimate causal quantities. The paper has two aims: first, to provide a consolidated overview of the statistical literature on causal inference for the data centric engineering community; and second, to illustrate how causal concepts and methods can be applied. The latter aim is achieved through Monte Carlo simulations designed to replicate typical empirical problems encountered in engineering research. R code for the simulations is made available for readers to run and adapt and citations are given to real world studies. Causal inference aims to quantify effects that occur due to explicit intervention (or 'treatment') in non-experimental settings, typically for non-randomly assigned treatments. The paper argues that analyses of engineering interventions are often characterized by such conditions, and consequently, that causal inference has immediate and valuable applicability

Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach

The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables. Such estimates, however, may suffer from the confounding/endogeneity bias due to omitted variables such as driving behaviour and weather. To this end, this paper adopts a causal approach to obtain an unbiased estimate of the fundamental flow-density relationship using traffic detector data. In particular, we apply a Bayesian non-parametric spline-based regression approach with instrumental variables to adjust for the aforementioned confounding bias. The proposed approach is benchmarked against standard curve-fitting methods in estimating the flow-density relationship for three highway bottlenecks in the United States. Our empirical results suggest that the saturated (or hypercongested) regime of the estimated flow-density relationship using correlational curve fitting methods may be severely biased, which in turn leads to biased estimates of important traffic control inputs such as capacity and capacity-drop. We emphasise that our causal approach is based on the physical laws of vehicle movement in a traffic stream as opposed to a demand-supply framework adopted in the economics literature. By doing so, we also aim to conciliate the engineering and economics approaches to this empirical problem. Our results, thus, have important implications both for traffic engineers and transport economists