Attitudes and motivations of Economics students: Some recent evidence

There is disagreement amongst economists regarding whether economics students are more self-interested than other students in economic and non-economic contexts. Econometric analysis of the choice to share in a Prisoner´s Dilemma game suggests that it may not be economics students per se that have a lower probability of choosing share rather than compete, but instead that individuals with attitudes, motivations and values similar to those assumed by standard economic theory have a lower probability of choosing share. The experimental evidence here of 1,701 students suggests that it is the motivations and attitudes of subjects that are important for determining economic choices rather than simply whether the individual studies economics. The results confirm that a higher proportion of economics students have motivations in a game theory context that are similar to those assumed by standard economic theory, yet that their related general attitudes and values are not significantly different. Overall the results suggest that the assumptions of standard economic theory are appropriate for a subset of individuals, and for many individuals who do not study economics

Are people ethical? An experimental approach

Do ethical motivations and attitudes affect behaviour? We examine this issue in six Prisoner´s Dilemma and Prisoner´s Dilemma related games using an online experiment where individuals were asked to make choices and subsequently to express the motivations for their choices and their general attitudes. The experimental evidence of 1,701 students suggests that the motivations and attitudes of respondents regarding altruism, inequality aversion, reciprocity and aversion to lying are important for determining economic choices as well as self-interest. Econometric analysis of the choice to share indicates that ethical and self-interested motives are more important for determining choices than personal characteristics

Incorporating Ethics into Economics: Problems and Possibilities

In traditional economics the decision-making process for individuals has effectively no role for ethics as individuals are self-interested. The key concepts in economics which determine the role of ethics in the decision-making process are utility, rationality and methodological individualism and hence how these can be and are formulated and combined determines different roles for ethics in economics. Amitai Etzioni, Amartya Sen and John Broome use different definitions of these concepts and hence find different problems and possibilities for a greater role for ethics in economics. This paper integrates the different approaches of these authors and suggests a general mono-utility framework for incorporating ethics into economics whereby the concept of utility requires adaptation

Common scale valuations across different preference-based measures: estimation using rank data

Background: Different preference-based measures (PBMs) used to estimate Quality Adjusted Life Years (QALYs) provide di¤erent utility values for the same patient. Differences are expected since values have been obtained using different samples, valuation techniques and descriptive systems. Previous studies have estimated the relationship between pairs of PBMs using patient self-reported data. However, there is a need for an approach capable of generating values directly on a common scale for a range of PBMs using the same sample of general population respondents and valuation technique but keeping the advantages of the different descriptive systems. Methods: General public survey data (n=501) where respondents ranked health states described using subsets of six PBMs were analysed. We develop a new model based on the mixed logit to overcome two key limitations of the standard rank ordered logit model, namely, the unrealistic choice pattern (Independence of Irrelevant Alternatives) and the independence of repeated observations. Results: There are substantial differences in the estimated parameters between the two models (mean di¤erence 0.07) leading to di¤erent orderings across the measures. Estimated values for the best states described by di¤erent PBMs are substantially and significantly di¤erent using the standard model, unlike our approach which yields more consistent results. Limitations: Data come from a exploratory study that is relatively small both in sample size and coverage of health states. Conclusions: This study develops a new, �exible econometric model specifically designed to reflect appropriately the features of rank data. Results support the view that the standard model is not appropriate in this setting and will yield very different and apparently inconsistent results. PBMs can be compared using a common scale by implementation of this new approach

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM): valuation of CORE-6D

Background: The Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods: Rasch analysis was used to identify 11 plausible ‘emotional’ health states from CORE-6D (rather than conventional statistical design that would generate implausible states). By combining these with the 3 response levels of the physical item of CORE-6D, 33 plausible health states can be described, of which 18 were selected for valuation. An interview valuation survey of 220 members of public in South Yorkshire, UK, was undertaken using the time-trade-off method to value the 18 health states; regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results: A number of multivariate regression models were built to predict values for the 33 plausible health states of CORE-6D, using the Rasch logit value of the emotional health state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R squared 0.990) was finally selected, which can be used to predict utility values for all 927 states described by CORE-6D. Conclusion: The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM datasets. The new method for generating states may be useful for other instruments with highly correlated dimensions

Mapping Functions in Health-Related Quality of Life: Mapping From Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.

BACKGROUND: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. METHODS: Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. RESULTS: For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. CONCLUSIONS: OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

Using rank and discrete choice data to estimate health state utility values on the QALY scale

Objective: Recent years has seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off. However, in order to use these health state values in cost effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. This study addresses this challenge and examines how rank and discrete choice experiment data can be used to elicit health state utility values anchored on the full health-dead scale and compares the results to time trade-off (TTO) results. Methods: Two valuation studies were conducted using identical methods for two health state classification systems: asthma and overactive bladder. Each valuation study involved interviews of 300 members of the general population using ranking and TTO plus a postal survey using discrete choice experiment sent to all consenting interviewees and a "cold" sample of the general population who were not interviewed. Results: Overall DCE produced different results to ranking and time trade-off, whereas ranking produced similar results to TTO in one study, but not the other. Conclusions: Ordinal methods offer a promising alternative to conventional cardinal methods of standard gamble and TTO. However, the results do not appear to be robust across different health state classification systems and potentially different medical conditions. There remains a large and important research agenda to address

It's all in the name, or is it? The impact of labelling on health state values

Many descriptions of health used in vignettes and condition-specific measures refer to the medical condition. This paper assesses the impact of referring to the medical condition in the descriptions of health states valued by members of the general population. A sample of 241 members of the UK general population each valued 8 health states using time trade-off. All respondents valued essentially the same health states, but for each respondent the descriptions featured either an irritable bowel syndrome label, a cancer label or no label. Regression techniques were used to estimate the impact of each label and experience of the condition on health state values. We find that the inclusion of a cancer label in health state descriptions affects health state values and that the impact is dependent upon the severity of the state. A condition label can affect health state values, but this is dependent upon the specific condition and severity. It is recommended to avoid condition labels in health state descriptions (where possible) to ensure that values are not affected by prior knowledge or preconception of the condition that may distort the health state being valued