We employ an appropriate change of measure technique to offer a general result connecting a general form of the Gerber–Shiu function with the distribution of the deficit at ruin under the new (exponentially tilted) measure. Exploiting this result, we extract closed-form formulae for special forms of the Gerber–Shiu function assuming two cases of bivariate distributions that describe the dependence structure between claim sizes and inter-claim times. More specifically, initially, we employ the Downton–Moran bivariate exponential distribution, and we offer explicit formulae for cases of the Gerber–Shiu functions that include the time and the number of claims until ruin. In addition, we derive a closed formula for the defective discounted joint density of the number of claims until ruin, the deficit at ruin, and the time until ruin. The same is achieved for the joint density of the number of claims and the deficit at ruin. We further generalize these results by assuming that the inter-claim times and the claim sizes follow a Kibble–Moran bivariate Erlang distribution. Finally, we offer numerical examples in order to illustrate our main results.

Public support is crucial for the success of policy interventions that aim to change behaviour. While communicating evidence of policy effectiveness can increase support, it remains unclear which type of evidence is most effective. Statistical evidence is often seen as objective and persuasive, yet personal anecdotes can strongly influence beliefs. We examined how statistical and anecdotal evidence affect policy perceptions. In three online experiments with representative UK samples (N = 908), we showed participants different types of evidence (statistical, anecdotal, or both) that argued for or against six policies, such as meat taxes (climate change), banning e-cigarettes (public health), and 20 mph speed limits (community safety). We measured policy support and perceived effectiveness before and after exposure and explored participants’ reasoning through open-text responses. Results showed that positive statistical and anecdotal evidence did not significantly increase perceived policy effectiveness or support, even when combined. However, negative anecdotes significantly reduced both, though this effect was sometimes mitigated when paired with statistical evidence. Qualitative results found that participants have broader concerns beyond policy effectiveness, such as fairness. Our findings suggest that communicating evidence on policy effectiveness alone may not increase support, as it does not address broader public concerns.

We evaluate the effect of reciprocal trust within pairs of individuals—gauged by total potential earnings in a trust experiment—on the probability of relationship formation, in comparison with well-known determinants of social ties, such as time of exposure and homophily along demographic traits. We measured trust and trustworthiness for every individual in an incoming cohort of undergraduate students before they began interacting. Using relationship data sourced from surveys and campus entry/exit times between one month and two years after the trust experiment, we find that reciprocal trust is neither a statistically nor an economically significant factor in determining the students’ social networks. Instead, time of exposure, prior acquaintance, and other demographic characteristics play important and persistent roles in relationship formation.

We investigate the impact of the academic environment on directed technical change and economic growth. We develop a heterogeneous Schumpeterian growth model in which innovation is categorized into radical and incremental types. A supportive environment for academic exploration enhances scientists’ autonomy utility in basic research, thereby motivating basic research and reducing the R&D difficulty of radical innovations through knowledge spillovers. We identify two major effects of the academic environment on economic growth: a positive directed technical change effect fostering growth through radical innovation, and a negative applied research crowding-out effect. Numerical analysis based on Chinese data reveals a negative autonomy utility (−0.37), indicating insufficient autonomy in basic research exploration. Promoting economic growth necessitates institutional reforms. The optimal autonomy utility for maximizing growth is 0.80. Welfare analysis further shows that the optimal autonomy utility is 0.87 for basic research labor and 0.75 for non-basic research labor.

This paper estimates the impacts of an experiment that provided Canadian high-school students with exogenous amounts and types of financial aid. We collected data ten years later to assess short and long-run effects on educational and financial trajectories. While the average impacts on enrollment, graduation, debt, and earnings appear to be limited in the overall sample, we find that loan offers have heterogeneous effects in different institutional environments, shifting students from two to four-year programs in Quebec while producing the opposite effect in other provinces. Our results also reveal that parents respond to financial aid by increasing their transfers, especially outside of Quebec and among students coming from lower-educated backgrounds. Among the latter population, we additionally find that raising grant amounts by $1,000 reduces the probability of completing the first program of study by 14pp, an effect that is not observed in the rest of the sample.

We revisit the question of how to include parameter uncertainty in univariate parametric models of losses and loss ratios. We first review the statistical theory for including parameter uncertainty based on right Haar priors (RHPs), which applies to many commonly used models. In this theory, the prior is chosen in such a way as to ensure matching between predicted probabilities and the relative frequencies of future outcomes in repeated tests. This property is known as reliability, or calibration. We then test priors for including parameter uncertainty in a number of models not covered by RHP theory. For these models, we find priors that generate predictions that are more reliable than predictions based on maximum likelihood, although they are not perfectly reliable. We discuss numerical schemes that can be used to generate Bayesian predictions, including a novel use of asymptotic expansions, and we include an example in which we show the impact of including parameter uncertainty in the modeling of extreme hurricane losses. The tail loss estimates show material increases due to the inclusion of parameter uncertainty. Finally, we describe a new software library that makes it straightforward to apply the methods we describe.

Traditionally, many meat demand analyses have used publicly available data amassed by the U.S. Department of Agriculture (USDA) and the Bureau of Labor Statistics (BLS). Circana retail point-of-sale scanner data offer an alternative to these publicly available data sources. Scanner data allow for quantity-weighting retail prices to account for increased purchases at lower prices due to sales and promotions. We find that quantity-weighted scanner-based beef and pork prices are lower than those reported by USDA, whereas quantity-weighted chicken prices are higher. Rotterdam demand models are estimated using both publicly available and scanner data sources. Own- and cross-price elasticities estimated using scanner data are greater in magnitude than those estimated using publicly available data, suggesting meat consumers may be more price sensitive than indicated by elasticity estimates from publicly available data sources. Scanner data insights are further explored by estimating demand for meat products with organic or natural claims. Demand elasticities for these differentiated meat products are more elastic than those for meat products without such claims, highlighting a greater amount of consumer price sensitivity when purchasing such products.

The behavior of parent-incumbents depends not only on their own intention to hand over the family firm but also on their child-successors’ willingness to take over. Drawing on socioemotional wealth (SEW) theory, we develop a model of the impact of perceived child-successors’ willingness on parent-incumbents’ corporate philanthropy prior to succession. We argue that perceived child-successors’ willingness increases parent-incumbents’ transgenerational succession anticipation and the resulting desire to preserve SEW, which in turn motivates them to engage more in corporate philanthropy. However, parent-incumbents facing a greater threat of state expropriation engage less in philanthropy, as they anticipate a lower likelihood of successful transgenerational succession. Using data from a national survey of Chinese family firms, we find support for our hypotheses. Our findings highlight the significance of internal succession anticipation and its interplay with the external institutional environment in shaping family firm philanthropy.