Aidan Toner-Rodgers

This paper studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of "idea-generation" tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization.

Marginal Revolution, Macro Roundup, AEI

We propose a new, computationally efficient way to approximate the "grouped fixed effects" (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of unobserved heterogeneity. To do so, we generalize the fuzzy C-means objective to regression settings. As the clustering exponent approaches 1, the fuzzy clustering objective converges to the GFE objective, which we recast as a standard generalized method of moments problem. We replicate the empirical results of Bonhomme and Manresa (2015) and show that our estimator delivers almost identical estimates. In simulations, we show that our approach offers improvements in terms of bias, classification accuracy, and computational speed.

This paper studies the effects of natural disasters on foreign direct investment, considering the case of India. We document large and persistent investment reductions in affected regions following a disaster as well as lasting positive spillovers into otherwise unaffected Indian regions. Intra-national relocations account for more than two-thirds of the losses in affected areas, explaining the puzzlingly small country-level findings of previous works. Furthermore, we show that these investment shifts tend to flow into more developed, less disaster-prone regions, fueling the prominent divergence in India's economic growth. Combined, our results suggest that multinational firms consider both local cost and region-specific disaster risk when selecting locations for production.

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