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Since August 2024, I have been working at the European Commission as a Policy Officer in Multi-Criteria Decision Analysis and Public Policy at the Joint Research Center. Before that, I received my doctoral degree from ETH Zurich in 2024.

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I work at the intersection of research and public policy. In policy, I focus on decision analysis, governance, and evidence-based policymaking. In academia, I study how new technologies, particularly artificial intelligence, are adopted and how they shape the economy, using methods from operations research and macroeconomic analysis. 

Profilbild

Policy Projects

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1 / Intergenerational Fairness

Working and collaborating with the UN Beyond Lab on the Development of the Futures Balance Tool.

2 / Multi Criteria Decision Analysis

Supporting Impact Assessments using Social Multi Criteria Evaluation.

3 / Governance of Artificial Intelligence

General Interest in questions related to Artificial Intelligence, Economics and Policy.

Current Research

On the Effectiveness of Manufacturing Flexibility at the Worker Level

Major Revision Management Science
with Daniel Kwasnitschka, Henrik Franke and Fabian Sting

 This paper examines how manufacturing productivity is affected by increased flexibility through a two-year field experiment at an automotive supplier. Using smartwatches to enable flexible machine–worker assignments, we employ a difference-in-differences design to analyze 1.38 million machine status reports and 54,145 quality management entries. Contrary to theoretical predictions, we find no significant aggregate effect of flexibility on productivity. However, granular analysis reveals critical worker-level heterogeneity: while the productivity of machines operated by specialists declined, those operated by versatilists improved, and those operated by generalists showed no significant gains. By analyzing individual switching behaviors, we identify a new mechanism between flexibility and productivity, formalized in a parsimonious analytic model. Our findings demonstrate the importance of considering human factors and specific worker types when implementing pooling regimes in real-world manufacturing organizations.

Considering human motivation, is it more effective to assign work tasks to shop floor workers or let them choose among a set of open tasks? This paper reports the results of a field experiment conducted with a manufacturer that uses wearable devices to distribute shop floor tasks. Drawing on theory on human motivation, we analyze 66,233 machine status reports and 31,429 work tasks completed in two manufacturing plants in Germany and Italy. We rely on a Difference-in-Differences approach in a 13-week-long field experiment. The results show that allowing workers to choose their next task shows no aggregate productivity difference but reveals heterogeneous behavioral effects. We find that workers respond more slowly to tasks (response time), but once accepted, tasks are completed faster (completion time). We discuss the potentially canceling effects and uncover evidence for behavioral mechanisms such as social loafing. This study has significant consequences for production managers in charge of implementing choice-based digital task assignment systems.

Give Me a Choice! A Field Experiment on Task Choice Enabled by Wearables

Production and Operations Management, 2026
with Daniel Kwasnitschka, Henrik Franke and Torbjørn Netland

Artificial Intelligence as Self-Learning Capital
Economic Modeling, 2025
with Hans Gersbach and Evgenij Komarov

We present a tractable model of Artificial Intelligence (AI) as self-learning capital: Its productivity rises by its use. An AI sector and an applied research (AR) sector produce intermediates for a final good firm and compete for high-skilled workers---while benefitting from mutual spillovers. The economy displays a sequence of four tipping points: First, entrepreneurs and second, high-skilled workers drive the accumulation of self-learning AI. This is reversed in two subsequent tipping points. In the steady state, AI accumulates autonomously due to spillovers from AR. By characterizing the social planner's selection of tipping points, we obtain that suitable subsidy/tax policies induce socially optimal movements of workers. In particular, we provide a macroeconomic rationale for an AI-tax as soon as AI becomes mature and benefits strongly from its learning ability. Moreover, we observe an increasing income divergence due to the rise of AI.

About me

2014-2017

Bachelor of Science in Economics

LMU München

2017-2019

Master of Science in Statistics
Humboldt Universität Berlin

2020-2024

Doctor of Science in Macroeconomic Theory

ETH Zürich

You find my complete CV here:
Disclaimer: The views expressed on this website are my own and should not be attributed to the European Commission.

Contact

Where you find me...
How to reach me...

richard.maydell             ec.europa.eu

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