Cormac O'Dea Publications

Pareto Efficiency is a core assumption of most models of household decision-making. We test this assumption using a new dataset covering the retirement saving contributions of over a million U.S. individuals. While a vast literature has failed to reject household efficiency in developed countries, we find evidence of widespread inefficiency in our setting: retirement contributions are not allocated to the account of the spouse with the highest employer match rate. This lack of coordination cannot be explained by inertia, auto-enrollment, or simple heuristics. Instead, we find that indicators of weaker marital commitment correlate with the incidence of inefficient allocations.

We study heterogeneity in spending patterns around the time of retirement. Using rich consumption data from the Panel Study of Income Dynamics, and exploiting within-household spending variation, we systematically classify households into groups characterized by differences in consumption transitions at retirement. We decompose the overall spending changes into the contribution made by different subcomponents of consumption. We find that the households that increase their spending shift budget away from food and toward transportation, recreation, and trips. In contrast, those households for which spending falls reduce the budget share spent on transportation and food away from home, while increasing the share allocated to food at home and housing expenditures. Using a life-cycle model, we characterize the mechanisms capable of driving these observed patterns.

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 fillings. 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.

The onset of the Covid-19 pandemic has led to a dramatic reduction in employment and hours worked in the US economy. The decline can be measured using conventional data sources such as the Current Population Survey and in the number of individuals filing for unemployment. However, given the unprecedented pace of the ongoing changes to labor market conditions, detailed, up-to-date, high frequency data on wages, employment, and hours of work is needed. Such data can provide insights into how firms and workers have been affected by the pandemic so far, and how those effects differ by type of firm and worker wage level. It can also be used to detail – in real time – the state of the labor market.