Kai Maeckle

I am a Finance PhD student at the University of Mannheim.

I am on the 2025-2026 job market. My research interests include corporate finance, innovation, and climate finance with a focus on structural estimation.

Curriculum Vitae

Photo credits: Katrin Glückler

Job Market Paper

Short-Termist Carbon Emissions
Abstract: Carbon abatement investments are long-term in nature. Therefore, short-term profit pressure may distort these investments. Consistent with this idea, firms that just meet analysts’ short-term profit targets have about five percentage points higher growth in carbon emissions than firms that just miss. I estimate a quantitative model in which managers choose carbon emissions subject to optimal short-term incentives. Removing short-term incentives reduces firms’ profits by 0.43% and carbon emissions by 2.19%. In aggregate, short-termist carbon emissions are as large as total U.S. aviation emissions in 2022. My estimates suggest that short-termism is welfare-reducing via the carbon emissions channel.

Other Research

Friends with Drugs: The Role of Social Networks in the Opioid Epidemic (with Stefan Ruenzi)
Abstract: The current opioid epidemic is the worst drug epidemic the U.S. has ever faced. We examine the role of social connections in its spatial spread. Having many friendship links to counties with high opioid exposure positively correlates with overdose death rates. To address identification concerns, we exploit two quasi-natural experiments that led users to substitute illicit opioids for prescription drugs. Having more friends exposed to counties affected by these shocks leads to more overdose deaths. We also provide suggestive evidence that spillovers are influenced by both supply- and demand-side conditions, affect different stages of addiction, and are attenuated in counties with more cohesive local friendship networks. At a general level, our results show how social connections contribute to the spread of non-infectious diseases.

Patent Grant Bundles (with Larissa Ginzinger, Markus Huggenberger, and Stefan Ruenzi), draft available upon request
Abstract: Stock market-based measures of innovation have been widely adopted in recent years in studies published in leading economics, finance, and management journals. In this paper, we examine the most popular patent valuation measure developed by Kogan et al. (2017). We start by documenting that 80% of all patents are granted together with other patents to the same firm on the same day (patent bundling). We show that the correlation between estimated patent values and forward citations is affected by bundle size effects but is largely insensitive to stock market returns around patent issue dates. Similarly, the positive correlation between the estimated value of innovation and future firm growth is not driven by return information but to a large extent driven by book-to-market effects. We develop a generalized theoretical framework that incorporates patent bundling and demonstrate that stock returns can only be informative about the economic value of patents when the number of granted patents deviates from market expectations. Empirically, this seems to be the case for a relatively small subset of large surprise patent grants.

Work in Progress

Investor Diversification, Firm Value, and Welfare