Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We develop a method that embeds signals about consumers’ knowledge to evaluate prospective choice architecture policies. We analyze three proposals for U.S. Medicare prescription drug insurance markets: (i) menu restrictions, (ii) personalized information, and (iii) defaulting consumers to cheap plans. We link administrative and survey data to identify informed enrollment decisions that proxy for preferences of observationally similar misinformed consumers. Results suggest that each policy yields winners and losers, with the menu restrictions harmful to most but personalized information beneficial to most. These results are robust across signals of consumers’ knowledge but differ from the benchmark that excludes such signals.