LLM Time Machines: Valuing Digital Goods Over Time

Abstract

Digital goods generate a significant amount of consumer welfare, yet the magnitude of these welfare gains is hard to estimate due to the lack of prices since most of these goods are free to consumers. Moreover, to fully understand their impact and track the welfare gains over time, we must assess how their value has evolved since their introduction. This is particularly challenging as newly introduced digital goods have limited consumer awareness, and current perceptions often bias retrospective estimates of previous consumer valuations. We investigate the feasibility of using large language models (LLMs) to estimate the valuations of digital goods via incentive-compatible single binary discrete choice experiments. We benchmark LLMs against valuations obtained from these choice experiments on representative samples of US populations from 2016-24. We find that valuations generated by LLMs are similar to valuations estimated using humans and follow similar patterns over time. Moreover, LLMs can be potentially used to extrapolate, going back or forward in time. We conclude by offering some guidance on using LLMs to generate longitudinal data on the valuations of digital goods and other types of goods.