A translation invariant pure DEA model

YesThis communication complements the DEA model proposed by Lovell and Pastor (1999), by incorporating both positive and negative criteria in the model. As such, we propose a DEA model, known as pure DEA, using a directional distance function approach

Ventilation Rates and Airflow Pathways in Patient Rooms: A Case Study of Bioaerosol Containment and Removal

Most studies on the transmission of infectious airborne disease have focused on patient room air changes per hour (ACH) and how ACH provides pathogen dilution and removal. The logical but mostly unproven premise is that greater air change rates reduce the concentration of infectious particles and thus, the probability of airborne disease transmission. Recently, a growing body of research suggests pathways between pathogenic source (patient) and control (exhaust) may be the dominant environmental factor. While increases in airborne disease transmission have been associated with ventilation rates below 2 ACH, comparatively less data are available to quantify the benefits of higher air change rates in clinical spaces. As a result, a series of tests were conducted in an actual hospital to observe the containment and removal of respirable aerosols (0.5–10 μm) with respect to ventilation rate and directional airflow in a general patient room, and, an airborne infectious isolation room. Higher ventilation rates were not found to be proportionately effective in reducing aerosol concentrations. Specifically, increasing mechanical ventilation from 2.5 to 5.5 ACH reduced aerosol concentrations only 30% on average. However, particle concentrations were more than 40% higher in pathways between the source and exhaust as was the suspension and migration of larger particles (3–10 μm) throughout the patient room(s). Computational analyses were used to validate the experimental results, and, to further quantify the effect of ventilation rate on exhaust and deposition removal in patient rooms as well as other particle transport phenomena

The role of energy productivity in the U.S. agriculture

This paper investigates the role of energy on U.S. agricultural productivity using panel data at the state level for the period 1960-2004. We first provide a historical account of energy use in U.S. agriculture. To do this we rely on the Bennet cost indicator to study how the price and volume components of energy costs have developed over time. We then proceed to analyze the contribution of energy to productivity in U.S. agriculture employing the Bennet-Bowley productivity indicator. An important feature of the Bennet-Bowley indicator is its direct association with the change in (normalized) profits. Thus our study is also able to analyze the link between profitability and productivity in U.S. agriculture. Panel regression estimates indicate that energy prices have a negative effect on profitability in the U.S. agricultural sector. We also find that energy productivity has generally remained below total farm productivity following the 1973-1974 global energy crisis

An economic approach to achievement and improvement indexes

This study proposes a useful alternative to the "aggregate deprivation index" which is used to measure the well-beings of individuals in different countries or geographic locations. Furthermore, we also propose an improvement index which alleviates well known difficulties associated with overtime comparisons of "aggregate deprivation index". While deriving our indexes, we pursued an economic approach to index numbers theory and relied on the assumptions of optimizing behavior. The proposed achievement index has its roots in the theory of quantity indexes whose axiomatic properties are well established. The roots of our improvement index on the other hand, is well grounded in the productivity growth literature. The study also provides a numerical example

Directional output distance functions: endogenous directions based on exogenous normalization constraints

In response to a question raised by Knox Lovell, we develop a method for estimating directional output distance functions with endogenously determined direction vectors based on exogenous normalization constraints. This is reminiscent of the Russell measure proposed by Färe and Lovell (J Econ Theory 19:150–162, 1978). Moreover it is related to the slacks-based directional distance function introduced by Färe and Grosskopf (Eur J Oper Res 200:320–322, 2010a, Eur J Oper Res 206:702, 2010b). Here we show how to use the slacks-based function to estimate the optimal directions.Keywords: Directional distance functions, Slack-based measures, DE

Accounting for externalities in the measurement of productivity growth: The Malmquist cost productivity measure

This paper starts with the basic premise: that conventional measures of productivity growth-often used as a measure of corporate performance-which ignore external or social output, are biased. We then construct an alternative productivity growth measure using activity analysis which integrates the externality/social output into a generalized productivity measure reflecting social responsibility. This method is very general and could be applied to gauge corporate social responsibility. We provide an application to US agriculture to demonstrate the approach: We show that conventional measures of productivity are biased upward when production of negative externalities (or bad) outputs is increasing. Conversely, this same measure of productivity is biased downward when externalities in production are decreasing. © 2004 Elsevier B.V. All rights reserved

Thomson scattering from a shock front

We have obtained a Thomson scattering spectrum in the collective regime by scattering a probe beam from a shock front, in an experiment conducted at the Omega laser at the Laboratory for Laser Energetics. The probe beam was created by frequency converting a beamline at Omega to a 2 ns2ns pulse of 0.263 μm0.263μm light, focused with a dedicated optical focusing system. The diagnostic system included collecting optics, spectrometer, and streak camera, with a scattering angle of 101°. The target included a primary shock tube, a 20-μm20-μm-thick beryllium drive disk, 0.3-μm0.3-μm-thick polyimide windows mounted on a secondary tube, and a gas fill tube. Detected acoustic waves propagated parallel to the target axis. Ten laser beams irradiated the beryllium disk with 0.351 μm0.351μm light at 5×1014 W/cm25×1014W∕cm2 for 1 ns1ns starting at toto, driving a strong shock through argon gas at ρo = 1 mg/ccρo=1mg∕cc. The 200 J200J probe beam fired at t = 19 nst=19ns for 2 ns2ns, and at t = 20.1 nst=20.1ns a 0.3 ns0.3ns signal was detected. We attribute this signal to scattering from the shocked argon, before the density increased above critical due to radiative collapse.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87893/2/10E504_1.pd

Reconstructing nonparametric productivity networks

Network models provide a general representation of inter-connected system dynamics. This ability to connect systems has led to a proliferation of network models for economic productivity analysis, primarily estimated non-parametrically using Data Envelopment Analysis (DEA). While network DEA models can be used to measure system performance, they lack a statistical framework for inference, due in part to the complex structure of network processes. We fill this gap by developing a general framework to infer the network structure in a Bayesian sense, in order to better understand the underlying relationships driving system performance. Our approach draws on recent advances in information science, machine learning and statistical inference from the physics of complex systems to estimate unobserved network linkages. To illustrate, we apply our framework to analyze the production of knowledge, via own and cross-disciplinary research, for a world-country panel of bibliometric data. We find significant interactions between related disciplinary research output, both in terms of quantity and quality. In the context of research productivity, our results on cross-disciplinary linkages could be used to better target research funding across disciplines and institutions. More generally, our framework for inferring the underlying network production technology could be applied to both public and private settings which entail spillovers, including intra-and inter-firm managerial decisions and public agency coordination. This framework also provides a systematic approach to model selection when the underlying network structure is unknown

On the robustness of gender differences in economic behavior

This is the final version. Available on open access from Nature Research via the DOI in this recordData and code availability: The dataset generated and analyzed for this research project as well as the custom code that supports the study′s findings are available on OSF (https://osf.io/tyzjh/?view_only=243e2b6ca1174a8d802f496ce97c6a70). The oTree code is available on request.Because of the importance of economic decisions, researchers have looked into what factors influence them. Gender has received a lot of attention for explaining differences in behavior. But how much can be associated with gender, and how much with an individual's biological sex? We run an experimental online study with cis- and transgender participants that (1) looks into correlational differences between gender and sex for competitiveness, risk-taking, and altruism by comparing decisions across these different subject groups. (2) we prime participants with either a masculine or feminine gender identity to examine causal gender effects on behavior. We hypothesize that if gender is indeed a primary factor for decision-making, (i) individuals of the same gender (but different sex) make similar decisions, and (ii) gender priming changes behavior. Based on 780 observations, we conclude that the role of gender (and sex) is not as decisive for economic behavior as originally thought.Elite Network of BavariaBavarian State Ministry of Science and the ArtsUniversity of Exeter Business Schoo