The “annuity puzzle” refers to the fact that annuities are rarely purchased despite the longevity insurance they provide. Most explanations for this puzzle assume that individuals have accurate expectations about their future survival. We provide evidence that individuals misperceive their mortality risk, and study the demand for annuities in a setting where annuities are priced by insurers on the basis of objectively-measured survival probabilities but in which individuals make purchasing decisions based on their own subjective survival probabilities. Subjective expectations have the capacity to explain significant rates of non-annuitization, yielding a quantitatively important explanation for the annuity puzzle.
Recent literature suggests the power of interventions to change habits. In a dense slum in Nairobi, we adopt best practices from the habit literature to encourage toilet use instead of alternatives that damage community health. Offering subsidies increased toilet usage, effects continue for one month after discounts end, but erode thereafter. Treatments designed to induce habit formation (marketing, time-limited discounts encouraging repetition, discounts for longer periods, targeting `habitual types’) generated no greater persistence. We see some persistent behavior change due to learning about the new toilet option. It appears difficult to induce pro-social behavior without private benefits through habit change.
This paper studies optimal bundling of products with inter-dependent values. I show that, under some conditions, a firm optimally chooses to sell only the full bundle of a given set of products if and only if the optimal sales volume of the full bundle is larger than the optimal sales volume for any smaller bundle. I then provide an interpretation of this characterization based on (i) the magnitude of the variation across consumers in how complementary they find different products, and (ii) how this variation correlates with price sensitivity.
We study reward-based crowdfunding, a new class of dynamic contribution games where a private good is produced only if the funding goal is reached by a deadline. Buyers face a problem of coordination rather than free-riding. A long-lived donor may alleviate this coordination risk, signaling his wealth through dynamic contributions. We characterize platform-, donor-, and buyer-optimal equilibrium outcomes, attained by Markov equilibria with simple donation strategies. We test the model’s predictions using high-frequency data collected from the largest crowdfunding platform, Kickstarter. The model fits the data well, especially for predictions concerning comparative statistics, donation dynamics, and properties of successful campaigns.
This paper studies the estimation and inferences in panel threshold regression with unobserved individual-specific threshold effects which is important from the practical perspective and is a distinguishing feature from traditional linear panel data models. It is shown that the within-regime differencing in the static model or the within-regime first-differencing in the dynamic model cannot generate consistent estimators of the threshold, so the correlated random effects models are suggested to handle the endogeneity in such general panel threshold models. We provide a unified estimation and inference framework that is valid for both the static and dynamic models and regardless of whether the unobserved individual-specific threshold effects exist or not. Especially, we propose alternative inference methods for the model parameters, which have better theoretical properties than the existing methods. Simulation studies and an empirical application illustrate the usefulness of our new estimation and inference methodology in practice.
We study robust welfare comparisons of learning biases, i.e., deviations from correct Bayesian updating. Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static (one signal) and dynamic (many signals) settings. While the static characterization compares posteriors signal-by-signal, the dynamic characterization employs an “efficiency index” quantifying the speed of belief convergence. Our results yield welfare-founded quantifications of the severity of well-documented biases. Moreover, the static and dynamic rankings can conflict, and “smaller” biases can be worse in dynamic settings.