This paper introduces a multidimensional theoretical perspective for predicting shareholder activism. First, I outline a generalized process of activist targeting, divided into two stages: target identification and target engagement. Second, I argue that activist shareholders target firms based on three elements: problem, solution, and intervention. Synthesizing several strands of literature and insights from the target identification stage of the activist targeting process, I develop the baseline proposition that activists do not merely target firms with clear problems, but account for the availability of viable solutions and the opportunity to intervene in their targeting calculus. Third, I extend this problem 3 solution 3 intervention (PSI) model to develop propositions about variation in the types and outcomes of activism campaigns that manifest in different contexts. Drawing on insights from the target engagement stage of the activist targeting process, I argue that activism campaigns vary in terms of both tactics and outcomes based on the configuration of the three elements of the PSI model.
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.
