The Evolution of U.S. Firms’ Retirement Plan Offerings: Evidence from a New Panel Data Set
This paper documents, using a newly-constructed data set, the evolution of the characteristics of employer-sponsored DC schemes. The features we focus on are their match schedules, vesting schedules, and the extent of ‘auto-features’ (i.e. presence of auto-enrollment, the level of any default contribution, and presence and details of auto-escalation). The data we construct is formed by hand-coding the details in narrative plan descriptions attached to plan filings. Our data covers approximately 5,000 plans, covering up to 37 million participants annually, for the period 2003-2017. We document that matching schedules, when they are offered, have become more generous over time. However, the proportion of firms offering a match fell sharply during the Great Recession and the proportion offering one did not recover to its pre-financial crisis level for almost a decade. Vesting schedules for DC plans have remained essentially unchanged since 2003, while the proportion of plans with auto-enrollment has increased dramatically over the same period. We find that the vast majority of plans that offer auto-enrollment have a default rate that is substantially lower than the level that would fully exploit the match offered by the employers.
Arnoud (International Monetary Fund), Choukhmane (MIT), Colmenares (MIT), O’Dea (Yale University), Parvathaneni (Yale University). The research reported herein was performed pursuant to grant RDR18000003 from the US Social Security Administration (SSA) funded as part of the Retirement and Disability Research Consortium. The opinions and conclusions expressed are solely those of the authors and do not represent the opinions or policy of SSA, any agency of the Federal Government, or NBER. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the contents of this report. Reference herein to any specific commercial product, process or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply endorsement, recommendation or favoring by the United States Government or any agency thereof. We are very grateful to the Social Security Administration for funding this and to the Yale Economics Tobin Center for Economic Policy for providing co-funding. We are grateful to Ryan Bubb for helpful conversations. Jun-Davinci Choi, Alessa Kim-Panero, Rosa Kleinman and Charlotte Townley provided excellent research assistance throughout our data collection. We are also grateful to Keelan Beirne, Rachel Bitustky, Jasper Feinberg, Albert Gong, Melissa Kim, Liana Wang, Clara Lew-Smith and Kelly Wei who provided excellent research assistance during parts of our data collection. We are grateful to Shamelle Richards for assistance with aspects of ERISA law. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.