Rebecca McDonald

Many public policies directly affect an individual's mortality risk. The value of longevity gains has traditionally been monetized through three different concepts: value per statistical life (VSL), value per statistical life year (VSLY), and willingness‐to‐pay for a quality‐adjusted life year (WTP–QALY). By adopting gains in life expectancy as the unifying metric and combining it with preference‐based information on health and longevity, we set out an approach that would generate three measures that are conceptually linked at the individual level. In particular, explicitly integrating preferences over health impacts would allow for the WTP–QALY to be estimated alongside the VSL and VSLY. We argue that our approach has advantages over the direct and modeling methods developed in the literature to date. In addition, we clarify how using the proposed framework could deliver values allowing for consistent decision‐making across policy domains and government departments in ex ante regulatory assessment while simultaneously honoring the different empirical estimation practices across government departments.

This paper examines how outcome modality in intertemporal choice influences time preferences and whether the process differs across cultures, specifically Japan and the United States. Uni-modal choices are those when the outcomes being compared over time are very similar, and cross-modal choices are those when the outcomes are very different. The cross-modal effect, previously shown in the U.S., is that there is greater patience in cross-modal decisions. In Experiment 1, we employed a between-participants design, in which participants either made uni-modal or cross-modal decisions. In Experiment 2, we employed a within-participants design in which everyone made both types of decision. In both Experiments we replicated the cross-modal effect. Moreover, the magnitude of the effect did not vary with factors known to relate to time preference, such as cognitive ability and social status, and it did not differ across cultures, even though Japanese participants were much more patient than American ones. The effect was stronger in the between- than within-participants experiment. These results strengthen the conclusion that the cross-modal effect is universal and strengthens the argument that it is due to the fundamental process of attentional dilution.

This article uses the well-being valuation (WV) approach to estimate and monetize the well-being impacts of informal care provision on caregivers. Using nationally representative longitudinal data from the UK, the British Household Panel Survey, we address two challenging methodological issues related to the economic valuation of informal care: (i) the anticipatory nature of informal care; and (ii) the sensitivity of income estimates used in valuation. We address the anticipatory issue by focusing on well-being impacts associated with caring for a relative who had recently suffered a serious accident. We use the fixed effects filtered (FEF) estimator to estimate a “time-invariant income” coefficient free from individual fixed effects bias, which helps to partially improve the quality of the income estimate as an alternative to using instrumental variables. This estimate is used in the calculation of shadow prices of informal care. Our estimates suggest that, focusing on the first year of unanticipated care provision, those experiencing the well-being losses from providing unanticipated informal care would be willing to pay approximately £13,167 on average to avoid it.

This paper examines the effectiveness of social recommendations that rely on providing descriptive social information, compared to random recommendations, in terms of changing behavior. To this end, we collected choices made by Deciders under three (Study 1) or two (Study 2) recommendation-source treatments and a no-recommendation baseline. After role assignment (Decider or Receiver), Deciders were presented with 12 choice problems, each requiring them to decide between two options (A and B) that either impacted the points they could earn for themselves or the points that could be earned by themselves and/or by one or two Receivers. At the end of the experiment, points were translated into monetary earnings for one randomly selected choice problem.

Information about individual behaviour is collected regularly by organisations. This information has value to businesses, the government and third parties. It is not clear what value this personal data has to consumers themselves. Much of the modern economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. One technique to assess how much individuals value their privacy is to ask them whether they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing personal data. Our research extends previous work on factors affecting individuals’ decisions about whether to share personal data. We take an experimental approach and focus on whether consumers place a positive value on protecting their data by examining their willingness to share personal data in a variety of data sharing environments. Using five evaluation techniques, we systematically investigate whether members of the public value keeping their personal data private. We show that the extent to which participants value protecting their information differs by data type, suggesting there is no simple function to assign a value for individual privacy. The majority of participants displayed remarkable consistency in their rankings of the importance of different types of data through a variety of elicitation procedures, a finding consistent with the existence of stable individual privacy preferences in protecting personal data. We discuss our findings in the context of research on the value of privacy and privacy preferences.

Personal data is key for the digital economy, underpinning business models and service provision. This information has value to businesses, the government and third parties, yet it is not clear what value this personal data has to consumers themselves. Much of the digital economy is predicated on people sharing personal data, however if individuals value their privacy, they may choose to withhold this data unless the perceived benefits of sharing outweigh the perceived value of keeping the data private. Further, they might be willing to pay for an otherwise free service if paying allowed them to avoid sharing their data. We used responses from a nationally representative sample of 2,087 UK residents to estimate public preferences towards sharing different types of personal data with different attributes. For example, we examined the differences in responses when data is shared for different purposes, as well as when it is shared with more or less granular details about identity or location. We found that people prefer to share their data with universities rather than with firms, and demand higher compensation if their data are shared with a higher level of detail, are linked to their identity, or are linked to their exact location. By integrating such preferences into business and regulatory models, we can create fairer and more balanced data sharing models, which can ultimately help people to make better choices about their personal data.