Influence of perceived risk on entrepreneurial desirability and feasibility: multidimensional approach for nascent entrepreneurs

This study examines the influence of perceived risk on entrepreneurial desirability and feasibility as determining factors in the intention to start a self-owned business. Specifically, a multidimensional approach is taken to examine the different types of risks associated with entrepreneurship: economic, social, time, health and personal risks. The results obtained from a sample of 376 new entrepreneurs in Mexico confirm the fact that the perceived desirability and feasibility have a significant and positive effect on the intentions to start their own company. On one hand, the empirical evidence obtained shows a heterogenic effect of the risk dimensions associated with entrepreneurship on the perceived desirability and feasibility in their behaviour. Therefore, it was found that the economic risk associated with entrepreneurship has a negative effect on the feasibility to start a business, but does not significantly influence the desirability of that behaviour. On the other hand, a significant effect from the risk related to health is not seen on desirability and feasibility, but the negative influence of personal risk is empirically supported for both variables. Finally, the results obtained related to social and time risk are contradictory. Social risk negatively influences entrepreneurship desirability, but a positive effect is observed on the feasibility to create a business. For its part, the empirical evidence obtained does not support any effect of the time risk on the perceived feasibility of starting a business, but there is a positive on entrepreneurship desirability. These results, which are contrary to the traditional concept of risk as a barrier to entrepreneurship, are justified by the vocational nature of this behaviour and by the social sacrifices that are sometimes necessary to be able to make a business work.     

Assessing the Impact of Fair Trade Coffee: Towards an Integrative Framework

This article presents an impact assessment framework that allows for the evaluation of positive and negative local-level impacts that have resulted from “responsible trade” interventions such as fair trade and ethical trade. The framework investigates impact relating to (1) livelihood impacts on primary stakeholders; (2) socio-economic impacts on communities; (3) organizational impacts; (4) environmental impacts; (5) policies and institutional impacts; and (6) future prospects. It identifies relevant local-level stakeholders and facilitates the analysis of conflicting interests. The framework was developed in the context of, and is applied in this article to, the fair trade coffee industry in northern Nicaragua. It was designed, however, so that it can be applied across commodity sectors and responsible trade initiatives. It is able to do this by accommodating for differences in the social, environmental, political and institutional contexts of different areas, and by taking into account the distinct nature of an initiative’s overall objectives, different levels of intervention, and the full range of stakeholders involved.     

Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks

Corporate bankruptcy prediction has attracted significant research attention from business academics, regulators and financial economists over the past five decades. However, much of this literature has relied on quite simplistic classifiers such as logistic regression and linear discriminant analysis (LDA). Based on a large sample of US corporate bankruptcies, we examine the predictive performance of 16 classifiers, ranging from the most restrictive classifiers (such as logit, probit and linear discriminant analysis) to more advanced techniques such as neural networks, support vector machines (SVMs) and “new age” statistical learning models including generalised boosting, AdaBoost and random forests. Consistent with the findings of Jones et al. ( 2015 ), we show that quite simple classifiers such as logit and LDA perform reasonably well in bankruptcy prediction. However, we recommend the use of “new age” classifiers in corporate bankruptcy modelling because: (1) they predict significantly better than all other classifiers on both the cross‐sectional and longitudinal test samples; (2) the models may have considerable practical appeal because they are relatively easy to estimate and implement (for instance, they require minimal researcher intervention for data preparation, variable selection and model architecture specification); and (3) while the underlying model structures can be very complex, we demonstrate that “new age” classifiers have a reasonably good level of interpretability through such metrics as relative variable importances (RVIs).     

Anatomy is not destiny: A feminist analysis of the scholarship on women's leisure

Abstract

The extent of the scholarship on women has accelerated over the past 20 years, yet many questions remain unanswered concerning what is known about women and their leisure behavior. Social science research about women and leisure that analyzes the interaction between the environment and biological sex has aided in understanding leisure behavior more completely and has enabled researchers to conclude that anatomy is not destiny. An analysis focusing on the critical corrective, and transformative aspects of feminist research provides a way to examine the trends in research on women specifically concerning women and leisure. The purpose of this article is to examine the foundations of the emerging scholarship on women and leisure, especially the research on gender differences, from a feminist perspective. The emerging trends in feminist research and the “state of the art” concerning scholarship on women as it is occurring in other disciplines is examined in light of how research in other fields may offer guidance for leisure research.     

A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering

The probability of informed trading (PIN) is a commonly used market microstructure measure for detecting the level of information asymmetry. Estimating PIN can be problematic due to corner solutions, local maxima and floating point exceptions (FPE). Yan and Zhang [J. Bank. Finance, 2012, 36, 454–467] show that whilst factorization can solve FPE, boundary solutions appear frequently in maximum likelihood estimation for PIN. A grid search initial value algorithm is suggested to overcome this problem. We present a faster method for reducing the likelihood of boundary solutions and local maxima based on hierarchical agglomerative clustering (HAC). We show that HAC can be used to determine an accurate and fast starting value approximation for PIN. This assists the maximum likelihood estimation process in both speed and accuracy.     

An empirical analysis of air pollution emissions policy for the U.K. residential sector

In this paper an analysis of the U.K. government's air pollution emissions policy for the residential sector is undertaken. The analysis covers emissions arising from water and space heating, electric appliances and cooking appliances. Using an integrated economic-engineering model, the effects of the Energy Savings Trust and the imposition of Value Added Tax on residential fuel will be evaluated in terms of energy consumption and pollution emissions. It is found that the initial proposed policy was not sufficiently stringent to meet the government's environmental objectives, and that subsequent events have undermined the government's programme even further. Unless alternative policies are introduced, emissions from the residential sector will play an important role in jeopardizing the ability of the U.K. government to meet its international obligations.     

Typology formation across socio-economic indicators

Some decisions in planning and policy formation require the comparison of one region or country with another. If the entities being compared are known and have been demonstrated to be either similar or different, then valid comparisons can be made. Unfortunately, many comparisons are made on an ad hoc and often improper basis. The paper describes a method of typology formation which requires the modification and the use of the Euclidean distance measure calculated between countries over a series of social indicators. The potential usefulness of the method is demonstrated by reference to an example using indicators of educational system provision.