The detrimental impacts of heat and drought on global agriculture are well documented, but the financial consequences of associated crop losses remain underexplored. Here, we use a statistical model to quantify the economic impacts of agricultural losses in three major crops (maize, wheat, and soybeans) arising from changes in severe heat and drought conditions over 2007–2019, relative to a historical baseline period (1974–2004). We further link the heat-induced losses to individual carbon emitters. Globally, we estimate that heat and drought-induced yield impacts have resulted in $251 billion of total losses over 2007–2019. Least-developed countries experienced GDP-normalized losses more than seven times higher than rich nations (0.036% versus 0.005% of GDP). Aggregated over the same period, CO 2 emissions from the world’s richest 10% are linked to approximately $78 billion in financial losses from associated heat-induced crop yield declines. This represents about 54% of the total economic damages across all income groups and is over eight times the contribution of the poorest 50%. CO 2 emissions from Carbon Major companies are estimated to be associated with $119 billion in heat-induced agricultural financial losses over the study period. Global annual losses could rise from $20 billion in 2019 to $161 billion by 2100 under a high-emissions scenario (SSP3-7.0)—an eightfold increase—while a sustainable development pathway (SSP1-2.6) could avoid an estimated $105 billion of these damages. By linking climate-induced yield losses to financial outcomes, we provide a more tangible understanding of climate risks from food system impacts and strengthen the basis for loss and damage claims.
The chapter will open with examples of transformational games that utilize overt, explicit approaches to attitude and behavior change. While acknowledging the worthwhile intentions of such games and their potential utility for triggering reflection and action, this overview will present the central premise of the chapter: that there are a number of fundamental reasons why explicit approaches can backfire or be of limited utility for persuasion and that the use of more implicit, covert approaches to persuasion can be more effective. The ‘embedded design’ model presented in this chapter is particularly relevant for games attempting to engage players with sensitive or potentially threatening topics or to address attitudes or behaviors that themselves are implicit or unconscious.
We compute new estimates of Total Factor Productivity (TFP) growth in the five largest European economies. Our estimates account for positive profits and use firm surveys to proxy for unobserved changes in factor utilization. These novelties have a major impact: our estimated TFP growth series are substantially less volatile and less cyclical than the ones obtained with standard methods. Based on our approach, we provide annual industry-level and aggregate TFP series, as well as the first estimates of profit and utilization-adjusted quarterly TFP growth in Europe/
We study judicial in-group bias in Indian criminal courts using newly collected data on over 5 million criminal case records from 2010–2018. After classifying gender and religious identity with a neural network, we exploit quasi-random assignment of cases to judges to determine whether judges favor defendants with similar identities to themselves. In the aggregate, we estimate tight zero effects of in-group bias based on shared gender or religion, including in settings where identity may be especially salient, such as when the victim and defendant have discordant identities. Proxying caste similarity with shared last names, we find a degree of in-group bias, but only among people with rare names; its aggregate impact remains small.
Strategy scholars conventionally view imitation as a one-way knowledge transfer from an innovating firm to an imitating firm. We propose that being imitated also serves as a source of learning for the innovator. An innovator learns by being imitated-observing the imitator's choices and outcomes and comparing them to its own. A key challenge in such learning, and in vicarious learning more broadly, is causal ambiguity in identifying the factors that drive observed performance outcomes. We theorize that an innovator's learning by being imitated is particularly effective in overcoming causal ambiguity when it takes the form of vicarious experimentation, where an imitator's near-clone of the innovator's product functions as a quasi-experimental treatment from which the innovator can learn. This concept extends the logic of strategic experimentation by highlighting how firms can learn from experiments they do not control. We test our theory in the video game industry, demonstrating that vicarious experimentation enhances the quality of the innovator's next-generation product and offering support for the theorized boundary conditions under which this effect holds.
