Combat,casualties, and compensation: Evidence from Iraq and Afghanistan

Our research examines the effect of combat deployments to Iraq and Afghanistan on casualties and combat exposure compensation policy. We use restricted data from the Defense Manpower Data Center (DMDC) and Social Security Administration (SSA) to construct a panel of all US Active Duty service members having served at some point during the years 2001–2012. Casualties disproportionately occur at higher rates among (i) young, white, males, (ii) enlisted personnel, (iii) less educated personnel, and (iv) those in combat job types. Our estimates indicate that overall US military personnel who deployed in an individual year to Iraq or Afghanistan had a 45 per 100,000 higher probability of death than non-deployed military personnel who remained stateside. The increased fatal injury risk of deployed US military personnel is 15 times higher than the national average civilian workplace fatality rate, but roughly equal to the fatal injury risk faced in some of the most dangerous civilian occupations. Our estimates suggest a compensating wage differential equal to $861 per month would be appropriate in comparison to the current average of $1238 per month in danger pay provided to US military personnel deployed into combat zones. We recommend a revenue-neutral approach in adjusting the pay structure for military members. For example, the military should consider increasing bonuses or base pay while simultaneously decreasing danger pay by an equivalent amount. Furthermore, it may be beneficial to adjust danger pay by service or job type to reflect fatality risk more accurately.     

Ethical implications of text generation in the age of artificial intelligence

We are at a turning point in the debate on the ethics of Artificial Intelligence (AI) because we are witnessing the rise of general-purpose AI text agents such as GPT-3 that can generate large-scale highly refined content that appears to have been written by a human. Yet, a discussion on the ethical issues related to the blurring of the roles between humans and machines in the production of content in the business arena is lacking. In this conceptual paper, drawing on agenda setting theory and stakeholder theory, we challenge the current debate on the ethics of AI and aim to stimulate studies that develop research around three new challenges of AI text agents: automated mass manipulation and disinformation (i.e., fake agenda problem), massive low-quality content production (i.e., lowest denominator problem) and the creation of a growing buffer in the communication between stakeholders (i.e., the mediation problem).