Libertarian Quasi-Paternalism

In many settings, people’s choices vary based on seemingly arbitrary features of the choice environment. Policies that manipulate these features to improve decision-makers’ well-being are paternalistic – unless one takes the unrealistic view that these features are relevant from the perspective of the choosers’ preferences. In such settings, I propose that policy design can be less paternalistic if the only people assumed to be making mistakes are those whose choices are observed to vary based on the arbitrary feature of the choice environment. I discuss several characteristics of such “quasi-paternalistic” policy design and conclude by applying the principle of quasi-paternalism to the policy choice of nudges versus mandates

Rationalizations and mistakes: optimal policy with normative ambiguity

Behavior that appears to violate neoclassical assumptions can often be rationalized by incorporating an optimization cost into decision-makers' utility functions. Depending on the setting, these costs may reflect either an actual welfare loss for the decision-maker who incurs them or a convenient (but welfare irrelevant) modeling device. We consider how the resolution of this normative ambiguity shapes optimal policy in a number of contexts, including default options, inertia in health plan selection, take-up of social programs, programs that encourage moving to a new neighborhood, and tax salience

Optimal defaults with normative ambiguity

Default effects are pervasive, but the reason they arise is often unclear. We study optimal policy when the planner does not know whether an observed default effect reflects a welfare-relevant preference or a mistake. Within a broad class of models, we find that determining optimal policy is impossible without resolving this ambiguity. Depending on the resolution, optimal policy tends in opposite directions: either minimizing the number of non-default choices or inducing active choice. We show how these considerations depend on whether active choosers make mistakes when selecting among non-default options. We illustrate our results using data on pension contribution defaults

Revealed-preference analysis with framing effects

In many settings, decision makers’ behavior is observed to vary on the basis of seemingly arbitrary factors. Such framing effects cast doubt on the welfare conclusions drawn from revealed-preference analysis. We relax the assumptions underlying that approach to accommodate settings in which framing effects are present. Plausible restrictions of varying strength permit either partial or point identification of preferences for the decision makers who choose consistently across frames. Recovering population preferences requires understanding the empirical relationship between decision makers’ preferences and their sensitivity to the frame. We develop tools for studying this relationship and illustrate them with data on automatic enrollment into pension plans

Forecasting Algorithms for Causal Inference with Panel Data

Conducting causal inference with panel data is a core challenge in social science research. We adapt a deep neural architecture for time series forecasting (the N-BEATS algorithm) to more accurately predict the counterfactual evolution of a treated unit had treatment not occurred. Across a range of settings, the resulting estimator ("SyNBEATS") significantly outperforms commonly employed methods (synthetic controls, two-way fixed effects), and attains comparable or more accurate performance compared to recently proposed methods (synthetic difference-in-differences, matrix completion). Our results highlight how advances in the forecasting literature can be harnessed to improve causal inference in panel data settings