The Econometric SocietyEdited by: Marina Halac • Print ISSN: 0012-9682 • Online ISSN: 1468-0262
Econometrica: Jul, 2025, Volume 93, Issue 4
https://doi.org/10.3982/ECTA18492
p. 1449-1480
Vivek Bhattacharya|Gastón Illanes|Manisha Padi
Fiduciary duty aims to solve principal‐agent problems, and the United States is in the middle of a protracted debate surrounding the merits of extending it to all financial advisers. Leveraging a transaction‐level data set of deferred annuities and state‐level variation in common law fiduciary duty, we find that it raises risk‐adjusted returns by 25 bp and leads to a 16% decline in the entry of affected firms. Through the lens of a model of entry and advice provision, we show that this effect can be due to both an increase in fixed costs and an increase in the cost of providing low‐quality advice. We show how to disentangle these channels and find that both are empirically relevant. Counterfactual simulations show that further increases in the stringency of fiduciary duty monotonically improve advice quality.
Vivek Bhattacharya, Gastón Illanes, and Manisha Padi
This supplement contains material not found within the manuscript.
Vivek Bhattacharya, Gastón Illanes, and Manisha Padi
The replication package for this paper is available at https://doi.org/10.5281/zenodo.15191144. The authors were granted an exemption to publish parts of their data because either access to these data is restricted or the authors do not have the right to republish them. However, the authors provided the Journal with (or assisted the Journal to obtain) temporary access to the parts of the restricted data for which that was feasible. For the remaining restricted data, the authors included in the package a simulated or synthetic dataset that allows running their codes. The Journal checked the data and the codes for their ability to generate all tables and figures in the paper and approved online appendices. Whenever the available data allowed, the Journal also checked for their ability to reproduce the results. However, the synthetic/simulated data are not designed to produce the same results. Given the highly demanding nature of the algorithms, the reproducibility checks were run on a simplified version of the code, which is also available in the replication package.
July 5, 2026
