Desperately Seeking Finance? The Demand for Finance by Women Owned and Led Businesses

The study reported here addresses some issues on gender, entrepreneurship and finance that have been identified as problematic in the literature. For example, much of the research to date is based on the assumption of entrepreneurship as male entrepreneurship; few studies have controlled for structural characteristics that may impact on the relationship between owner gender and a venture's ability to raise finance; and women are less likely than men to seek growth and external financing. Through the conduct of in-depth semi-structured interviews, an attempt has been made to give `voice' to women's intrinsically interesting experiences as the enactment of a situated practice, and not just in comparison with the assumed norm of male entrepreneurial activity. The findings suggest that when variables such as individual and firm characteristics are controlled for, generalizations found in the literature may not be supported. Further, the paper highlights that neither women entrepreneurs nor their businesses are homogeneous in nature and that greater heterogeneity in the study of female entrepreneurship in general, and access to finance in particular, is required.

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.

Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.

Why do people non-demand reveal in hypothetical double referenda for public goods?

Hypothetical contingent valuation surveys used to elicit values for environmental and other public goods often employ variants of the referendum mechanism due to the cognitive simplicity and familiarity of respondents with this voting format. One variant, the double referendum mechanism, requires respondents to state twice how they would vote for a given policy proposal given their cost of the good. Data from these surveys often exhibit anomalies inconsistent with standard economic models of consumer preferences. There are a number of published explanations for these anomalies, mostly focusing on problems with the second vote. This article investigates which aspects of the hypothetical task affect the degree of nondemand revelation and takes an individual-based approach to identifying people most likely to non-demand reveal. A clear profile emerges from our model of a person who faces a negative surplus i.e. a net loss in the second vote and invokes non self-interested, non financial motivations during the decision process.

Necessary conditions for demand revelation in double referenda

This paper demonstrates a set of necessary conditions that should generate unbiased, internally consistent estimates of willingness to pay (WTP) from a double referendum mechanism. These conditions are also sufficient for demand revelation in an experimental laboratory environment. However, the control over the mechanism achieved in the lab may not be transferrable to the field and WTP estimates derived from field surveys may remain biased. (c) 2008 Elsevier Inc. All rights reserved.

An On Demand Queue Management Architecture for a Programmable Traffic Manager

A queue manager (QM) is a core traffic management (TM) function used to provide per-flow queuing in access andmetro networks; however current designs have limited scalability. An on-demand QM (OD-QM) which is part of a new modular field-programmable gate-array (FPGA)-based TM is presented that dynamically maps active flows to the available physical resources; its scalability is derived from exploiting the observation that there are only a few hundred active flows in a high speed network. Simulations with real traffic show that it is a scalable, cost-effective approach that enhances per-flow queuing performance, thereby allowing per-flow QM without the need for extra external memory at speeds up to 10 Gbps. It utilizes 2.3%–16.3% of a Xilinx XC5VSX50t FPGA and works at 111 MHz.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Wind generation output during cold weather-driven electricity demand peaks in Ireland

Recent cold winters and prolonged periods of low wind speeds have prompted concerns about the increasing penetration of wind generation in the Irish and other northern European power systems. On the combined Republic of Ireland and Northern Ireland system there was in excess of 1.5 GW of installed wind power in January 2010. As the penetration of these variable, non-dispatchable generators increases, power systems are becoming more sensitive to weather events on the supply side as well as on the demand side. In the temperate climate of Ireland, sensitivity of supply to weather is mainly due to wind variability while demand sensitivity is driven by space heating or cooling loads. The interplay of these two weather-driven effects is of particular concern if demand spikes driven by low temperatures coincide with periods of low winds. In December 2009 and January 2010 Ireland experienced a prolonged spell of unusually cold conditions. During much of this time, wind generation output was low due to low wind speeds. The impacts of this event are presented as a case study of the effects of weather extremes on power systems with high penetrations of variable renewable generation.

Intelligent demand response electric water heating to integrate wind power in Ireland

Dwindling fossil fuel resources and pressures to reduce greenhouse gas (GHG) emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is that supply instantaneously meets demand and that robust operating standards are maintained to ensure a consistent supply of high quality electricity to end-users. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management (DSM) with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating (EWH) has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper, a continuous Direct Load Control (DLC) EWH algorithm is applied in a liberalized market environment using actual historical electricity system and market data to examine the potential energy savings, cost reductions and electricity system operational improvements.

Response of residential electricity demand to price: The effect of measurement error

In this paper we present an empirical analysis of the residential demand for electricity using annual aggregate data at the state level for 48 US states from 1995 to 2007. Earlier literature has examined residential energy consumption at the state level using annual or monthly data, focusing on the variation in price elasticities of demand across states or regions, but has failed to recognize or address two major issues. The first is that, when fitting dynamic panel models, the lagged consumption term in the right-hand side of the demand equation is endogenous. This has resulted in potentially inconsistent estimates of the long-run price elasticity of demand. The second is that energy price is likely mismeasured.