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Recent decades have witnessed a rising interest in the measurement of inequality from a multidimensional perspective. This literature has however remained largely theoretical. This chapter presents an empirical application of a recent methodology and in doing so offers practical insights on how multidimensional inequality can be measured over two attributes (wealth and health) in the developing country context. Following Abul Naga and Geoffard (2006), a methodological framework allowing the decomposition of multidimensional inequality into two univariate Atkinson–Kolm–Sen equality indices and a third term measuring the association between the attributes is implemented. The methodology is then illustrated using data from the World Health Surveys 2002–2003. Specifically, this study presents the first comparative analysis on multidimensional inequality for a set of Middle East and North African (MENA) countries. Results reveal that the multidimensional (in-)equality indices tend to mimic the (in-)equality ordering of the wealth distributions as the latter are always less equally distributed than health. An empirical conclusion that emerges is that reducing the correlation between the attributes may help to reduce overall welfare inequality, specifically when socioeconomic inequality in health is pro-poor. The finding that the correlation between attributes has a significant contribution in the quantification of inequality has important policy implications since it reveals that it is not only wealth and health inequalities per se that matter in the measurement of welfare inequality but also the associations between them.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

Degradable mulch film (DMF) is a potential alternate to polyethylene (PE) mulching. In this regard, the purpose of this paper is to explore the effects and paths of natural disaster shock and risk aversion influencing farmers’ DMF adoption.

This research is conducted by collecting cross-sectional data of corn farmers in Zhangye, China. First, by using the Tobit model, the paper attempts to explore the effects of natural disaster shock and risk aversion influencing farmers’ DMF adoption. Second, IV-Tobit model is applied to deal with endogenous problems between risk aversion and DMF adoption. Additionally, the researchers used a moderating model to analyze feasible paths of natural disaster shock and risk aversion impacting farmers’ DMF adoption.

The outcomes show that natural disaster shock and risk aversion significantly and positively affect farmers’ DMF adoption. Though risk aversion plays a significant moderating effect in influencing farmers’ DMF adoption by natural disaster shock, the moderating effect has a serious disguising effect. By considering the heterogeneity of risk aversion, the paper further confirms that if the intensity of natural disaster shock is increased by one unit, the intensity of MDF adoption by farmers with high-risk aversion also tends to increase by 15.85%.

This study is the pioneer one, which is evaluating the intensity of farmers’ DMF adoption from adoption ratio, investment amount, labor input and adoption time. Additionally, the research provides important guidelines for policymakers to motivate medium and low-risk aversion farmers to adopt DMF.

The purpose of this paper is to investigate the relationships between clinicians’ and managers’ risk aversion and a range of variables related to the implementation of the Collaborative Recovery Model (CRM). Positive risk taking is an integral component of the recovery process. Clinicians’ risk aversion has the potential to negatively impact on their implementation of recovery-oriented practices. The CRM provides an evidence-based framework to assist consumers to participate in the recovery process. However, there is a need for research to clarify the factors related to recovery that have impact on managers’ and clinicians’ risk aversion, and ultimately on implementation of recovery practices.

A cross-sectional survey assessed clinicians’ (n=174) and managers’ (n=48) risk aversion and their self-reported learning experiences, commitment to using CRM, goal setting attitudes and CRM implementation behaviour.

Clinicians who reported more risk aversion were significantly more likely to report positive attitudes towards goal setting. Stepwise regression revealed that training experiences, goal setting attitudes and commitment to CRM significantly predicted an increase in CRM implementation. Over and above this, risk aversion predicted a small but significant increase in the self-reported use of CRM. Managers experienced significantly less risk aversion than clinicians, with a negative relationship between risk aversion and commitment to CRM principles.

This paper suggests that clinicians’ risk aversion impacts upon their implementation of the CRM, with managers less risk averse than clinicians.

The purpose of this paper is to investigate gender differences in risk aversion and determine the effect of personality traits on risk aversion from an African country context.

The study combined both descriptive and analytical designs, and employed t-test, Pearson’s χ² and binary logistic regression as the main analytical techniques. Data for the analysis were obtained from the World Bank’s Skills toward Employment and Productivity survey on Ghana.

Results of the study revealed systematic differences in personality and gender and their associations with risk aversion. Specifically, women were found to be more risk averse than men. Differences in personality also showed that females reported higher levels of personality in all but one of the Big Five personality traits – extraversion. In addition to gender, age and education, the personality traits of conscientiousness and stability were the main predictors of the likelihood of being risk averse. Although personality differences existed between male and female, the interaction terms between gender and personality factors were not statistically significant.

The paper departs from the extant literature on developed countries and western cultures to add to the understanding on how individual differences account for variation in revealed risk preferences.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

The paper examines several measures of multidimensional inequality, analysing their properties and majorisation criteria. Moreover, it presents a new measure which generalises Bourguignon (1999) and includes Tsui measures (1999), while preserving the advantages of Maasoumi's method (1986) of explicitly acknowledging the role of parameters relevant to multivariate settings. Finally, an application to Argentine data is provided in order to illustrate the decisions involved in the process of applying these measures and the usefulness of having appropriate criteria when making those decisions.