We conduct a field experiment with US households to study how expectations about long-run home price growth shape spending decisions. We exogenously vary survey respondents' expectations by providing different expert forecasts. Homeowners' spending, measured using rich home-scanner data, is inelastic to home price expectations. By contrast, renters reduce their spending when expecting higher home price growth. These patterns reflect positive wealth effects for owners from higher future wealth and negative income effects for both groups due to higher future housing costs. Our study highlights consequences of asset price growth and long-term expectations about the economy for household behavior.

Individuals might experience negative utility from not consuming a popular product. With such externalities to nonusers, standard consumer surplus measures, which take aggregate consumption as given, fail to appropriately capture consumer welfare. We propose an approach to account for these externalities and apply it to estimate consumer welfare from two social media platforms: TikTok and Instagram. Incentivized experiments with college students indicate positive welfare based on the standard measure but negative welfare when accounting for these nonuser externalities. Our findings high-light the existence of product market traps, where active users of a platform prefer it not to exist.

We propose a technique for assessing robustness to demand effects of findings from experiments and surveys. The core idea is that by deliberately inducing demand in a structured way we can bound its influence. We present a model in which participants respond to their beliefs about the researcher’s objectives. Bounds are obtained by manipulating those beliefs with “demand treatments.” We apply the method to 11 classic tasks, and estimate bounds averaging 0.13 standard deviations, suggesting that typical demand effects are probably modest. We also show how to compute demand-robust treatment effects and how to structurally estimate the model.