with Karun Adusumilli and Claudio Schilter
Dynamic decisions are pivotal to economic policy making. We show how existing evidence from randomized control trials can be utilized to guide personalized decisions in challenging dynamic environments with budget and capacity constraints. Recent advances in reinforcement learning now enable the solution of many complex, real-world problems for the first time. We allow for restricted classes of policy functions and prove that their regret decays at rate n−1/2, the same as in the static case. Applying our methods to job training, we find that by exploiting the problem’s dynamic structure, we achieve significantly higher welfare compared to static approaches.
with Xavier Jaravel
Revise & Resubmit, Review of Economic Studies
AI-generated podcast with NotebookLM summarising some key points of the paper: Link
Notebook to try own AI-led interviews within minutes and full platform code available at: https://github.com/friedrichgeiecke/interviews
The advent of large language models (LLMs) provides an opportunity to conduct qualitative interviews at a large scale, with thousands of respondents, creating a bridge between qualitative and quantitative methods. In this paper, we develop a simple, versatile open-source platform for researchers to run AI-led qualitative interviews. Our approach incorporates established best practices from the sociology literature, uses only a single LLM agent with low latency, and can be adapted to new interview topics almost instantaneously. We assess its robustness by drawing comparisons to human experts and using several respondents-based quality metrics. Its versatility is illustrated through four broad classes of applications: eliciting key factors in decision making, political views, views of the external world, and subjective mental states. High performance ratings are obtained in all of these domains. The platform is easy to use and deploy: we provide detailed explanations and code for researchers to swiftly set up and test their own AI-led interviews.
We use methods from natural language processing and machine learning to characterize the innovative content of patents. We elaborate the concept of widening innovations, which are patents whose textual content is different from existing patents at the time when they are filed and similar to patents subsequently filed. The term “widening'' reflects the idea that these patents extend knowledge in a novel way and spur innovation. The computations of patents' similarities rely on numerical representations of the textual content of patents. We show that widening patents have higher citations and the firms owning them make higher profits and grow faster relative to other firms, although causality between filing such a patent and firms' performance cannot be established. Unsurprisingly, many widening patents stem from the IT revolution, a field near-non-existent in the 1980s which became predominant in the innovation landscape.
with Tim Besley, Chris Dann, and Valentino Larcinese
How do political careers relate to family networks? We address this question using a novel dataset assembled from various sources and combining information on the political careers of British MPs with information about the family trees of the British aristocracy. Our data span two centuries (1832-2022) and document the evolution of backgrounds of British MPs over time, their social origin (in particular whether aristocratic or not), education (university and school attended), profession and political experience. Using information on family trees we can then calculate the centrality of those MPs who are part of the aristocratic network and assess the role of network centrality in election and career progression.
I introduce a simple mechanism through which perpetual fluctuations in aggregate output can result endogenously. Individuals share sentiments on a network similarly to diseases in models of disease transmission. The sentiment that is in line with current movements in output, i.e. optimism if output is rising and pessimism if output is declining, is relatively more infectious. Consumption of optimistic consumers is biased upwards and consumption of pessimistic consumers downwards. With pricing frictions, the resulting changes in aggregate demand change aggregate output. In turn, however, changes in aggregate output change relative infection probabilities. The interplay of the two forces creates a feedback mechanism with recurring waves of optimism and pessimism. Fluctuations in output are an inherent feature of this economy. With very small real i.i.d. disturbances, fluctuations become irregular. While the mechanism is more general, it is illustrated in a stylised behavioural New Keynesian model.