The demand for labor: An analysis using matched employer-employee data from the German LIAB. Will the high unskilled worker own-wage elasticity please stand up?

This paper uses matched employee-employer LIAB data to provide panel estimates of the structure of labor demand in western Germany, 1993-2002, distinguishing between highly skilled, skilled, and unskilled labor and between the manufacturing and service sectors. Reflecting current preoccupations, our demand analysis seeks also to accommodate the impact of technology and trade in addition to wages. The bottom-line interests are to provide elasticities of the demand for unskilled (and other) labor that should assist in short-run policy design and to identify the extent of skill biases or otherwise in trade and technology.

An Analysis of the Effect of Electric Vehicle Charging on the Operation of the Single Electricity Market

To meet European Union renewable energy and greenhouse gas emissions reduction targets the Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. However, reducing energy consumption and decreasing greenhouse gas emissions in transport is a considerable challenge due to heavy reliance on fossil fuels. In fact, transport in the Republic of Ireland in 2009 accounted for 29% of non-emissions trading scheme greenhouse gas emissions, 32% of energy-related greenhouse gas emissions, 21% of total greenhouse gas emissions and approximately 50% of energy-related non-emission trading scheme greenhouse gas emissions. In this paper the effect of electric vehicle charging on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is analysed. The energy consumed, greenhouse gas emissions generated and changes to the wholesale price of electricity under peak and off-peak charging scenarios are quantified and discussed. Results from the study show that off-peak charging is more beneficial than peak charging.

Job strain as a risk factor for coronary heart disease:a collaborative meta-analysis of individual participant data

BACKGROUND: Published work assessing psychosocial stress (job strain) as a risk factor for coronary heart disease is inconsistent and subject to publication bias and reverse causation bias. We analysed the relation between job strain and coronary heart disease with a meta-analysis of published and unpublished studies. METHODS: We used individual records from 13 European cohort studies (1985-2006) of men and women without coronary heart disease who were employed at time of baseline assessment. We measured job strain with questions from validated job-content and demand-control questionnaires. We extracted data in two stages such that acquisition and harmonisation of job strain measure and covariables occurred before linkage to records for coronary heart disease. We defined incident coronary heart disease as the first non-fatal myocardial infarction or coronary death. FINDINGS: 30?214 (15%) of 197?473 participants reported job strain. In 1·49 million person-years at risk (mean follow-up 7·5 years [SD 1·7]), we recorded 2358 events of incident coronary heart disease. After adjustment for sex and age, the hazard ratio for job strain versus no job strain was 1·23 (95% CI 1·10-1·37). This effect estimate was higher in published (1·43, 1·15-1·77) than unpublished (1·16, 1·02-1·32) studies. Hazard ratios were likewise raised in analyses addressing reverse causality by exclusion of events of coronary heart disease that occurred in the first 3 years (1·31, 1·15-1·48) and 5 years (1·30, 1·13-1·50) of follow-up. We noted an association between job strain and coronary heart disease for sex, age groups, socioeconomic strata, and region, and after adjustments for socioeconomic status, and lifestyle and conventional risk factors. The population attributable risk for job strain was 3·4%. INTERPRETATION: Our findings suggest that prevention of workplace stress might decrease disease incidence; however, this strategy would have a much smaller effect than would tackling of standard risk factors, such as smoking. FUNDING: Finnish Work Environment Fund, the Academy of Finland, the Swedish Research Council for Working Life and Social Research, the German Social Accident Insurance, the Danish National Research Centre for the Working Environment, the BUPA Foundation, the Ministry of Social Affairs and Employment, the Medical Research Council, the Wellcome Trust, and the US National Institutes of Health.

A well-to-wheel analysis of electric vehicles in the all-island Single Electricity Market

The European Union has set a target for 10% renewable energy in transport by 2020 to be met using biofuels and electric vehicles. In the case of biofuels, the biofuel must achieve greenhouse gas savings of 35% relative to the fossil fuel replaced. For biofuels, greenhouse gas savings can be calculated using life cycle analysis or the European Union default values. In contrast, all electricity used in transport is considered to be the same, regardless of the source or the type of electric vehicle. However, the choice of the electric vehicle and electricity source will have a major impact on the greenhouse gas saving. In this paper the initial findings of a well-to-wheel analysis of electric vehicle deployment in Northern Ireland are presented. The key finding indicates that electric vehicles require least amount of energy per mile on a well-to-wheel basis, consume the fewest resources, even accommodating inefficient fuel production, in comparison to standard internal combustion engine and hybrid vehicles.

Efficiency assessment of electricity power generation on Ireland’s Single Electricity Market by Data Envelopment Analysis

The efficiency of generation plants is an important measure for evaluating the operating performance. The objective of this paper is to evaluate electricity power generation by conducting an All-Island-Generator-Efficiency-Study (AIGES) for the Republic of Ireland and Northern Ireland by utilising a Data Envelopment Analysis (DEA) approach. An operational performance efficiency index is defined and pursued for the year 2008. The economic activities of electricity generation units/plants examined in this paper are characterized by numerous input and output indicators. Constant returns to scale (CRS) and variable returns to scale (VRS) type DEA models are employed in the analysis. Also a slacks based analysis indicates the level of inefficiency for each variable examined. The findings from this study provide a general ranking and evaluation but also facilitate various interesting efficiency comparisons between generators by fuel type.

Does Cost-Effectiveness Analysis Have a Role in US Managed Care Drug Formularies? An Empirical Study of Utilization, Costs, Outcomes, and Elasticity of Demand of a Value-Based Formulary

Thesis (Ph.D.)--University of Washington, 2015

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Analysis of Electricity Market Prices Using Multidimensional Scaling

This paper studies the impact of the energy upon electricity markets using Multidimensional Scaling (MDS). Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, the results help electricity markets agents hedging against Market Clearing Price (MCP) volatility.

NAVA enhances tidal volume and diaphragmatic electro-myographic activity matching: a Range90 analysis of supply and demand.

Neurally adjusted ventilatory assist (NAVA) is a ventilation assist mode that delivers pressure in proportionality to electrical activity of the diaphragm (Eadi). Compared to pressure support ventilation (PS), it improves patient-ventilator synchrony and should allow a better expression of patient's intrinsic respiratory variability. We hypothesize that NAVA provides better matching in ventilator tidal volume (Vt) to patients inspiratory demand. 22 patients with acute respiratory failure, ventilated with PS were included in the study. A comparative study was carried out between PS and NAVA, with NAVA gain ensuring the same peak airway pressure as PS. Robust coefficients of variation (CVR) for Eadi and Vt were compared for each mode. The integral of Eadi (ʃEadi) was used to represent patient's inspiratory demand. To evaluate tidal volume and patient's demand matching, Range90 = 5-95 % range of the Vt/ʃEadi ratio was calculated, to normalize and compare differences in demand within and between patients and modes. In this study, peak Eadi and ʃEadi are correlated with median correlation of coefficients, R > 0.95. Median ʃEadi, Vt, neural inspiratory time (Ti_ ( Neural )), inspiratory time (Ti) and peak inspiratory pressure (PIP) were similar in PS and NAVA. However, it was found that individual patients have higher or smaller ʃEadi, Vt, Ti_ ( Neural ), Ti and PIP. CVR analysis showed greater Vt variability for NAVA (p < 0.005). Range90 was lower for NAVA than PS for 21 of 22 patients. NAVA provided better matching of Vt to ʃEadi for 21 of 22 patients, and provided greater variability Vt. These results were achieved regardless of differences in ventilatory demand (Eadi) between patients and modes.

Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a \"regular\" \"L-utility\" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and posterior uncertainty in terms of distributions over the indefinite-dimensional parameter set of a flexible functional form. We propose a class of proper priors on the parameter set. The priors are flexible, in the sense that they put positive probability in the neighborhood of any L-utility function that is regular on a large subset bar(X) of X; and regular, in the sense that they assign zero probability to the set of L-utility functions that are irregular on bar(X). We propose methods of Bayesian inference for an environment with indivisible goods, leaving the more difficult case of indefinitely divisible goods for another paper. We analyse individual choice data from a consumer experiment described in Harbaugh et al. (2001).