287 research outputs found
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
Quantifying the Uncertainty of Imputed Demographic Disparity Estimates: The Dual-Bootstrap
Measuring average differences in an outcome across racial or ethnic groups is
a crucial first step for equity assessments, but researchers often lack access
to data on individuals' races and ethnicities to calculate them. A common
solution is to impute the missing race or ethnicity labels using proxies, then
use those imputations to estimate the disparity. Conventional standard errors
mischaracterize the resulting estimate's uncertainty because they treat the
imputation model as given and fixed, instead of as an unknown object that must
be estimated with uncertainty. We propose a dual-bootstrap approach that
explicitly accounts for measurement uncertainty and thus enables more accurate
statistical inference, which we demonstrate via simulation. In addition, we
adapt our approach to the commonly used Bayesian Improved Surname Geocoding
(BISG) imputation algorithm, where direct bootstrapping is infeasible because
the underlying Census Bureau data are unavailable. In simulations, we find that
measurement uncertainty is generally insignificant for BISG except in
particular circumstances; bias, not variance, is likely the predominant source
of error. We apply our method to quantify the uncertainty of prevalence
estimates of common health conditions by race using data from the American
Family Cohort.Comment: 31 pages; 7 figures; CRIW Race, Ethnicity, and Economic Statistics
for the 21st Century, Spring 202
- …