UCLA

The dissertation consists of three chapters. The first chapter studies the identification and estimation of binary choices with hidden information diffusion. It investigates the hidden information diffusion and consumer preferences for a new product, where only informed individuals who have acquired product information can make adoption decisions. Due to unobserved information acquisition/diffusion, we cannot distinguish informed but unwilling-to-adopt individuals from uninformed ones. In addition to a random utility model, we introduce a binary threshold crossing model to characterize information diffusion: an individual becomes informed if the exposure to the information, which depends on the fraction of already informed neighbors and personal attributes, exceeds some idiosyncratic threshold. We show the identification of diffusion and utility parameters through two subsets of observations: (i) adoption behaviors of seeds (first-informed individuals) over time; (ii) adoption behaviors of seeds' neighbors at time two. We estimate diffusion and utility parameters using the subset of observations that yields the identification. With regularity conditions, we establish consistency and asymptotic normality of the estimator.

The second chapter applies the econometric framework developed in the first chapter to the diffusion of a microfinance program in rural India. We study the parametric estimation and have two major findings. First, communication with informed neighbors is crucial in acquiring the information. Second, the participation rate of neighbors has a non-monotone impact on the household's incentive to participate. We conduct a counterfactual analysis based on model estimates and evaluate the performance of many seeding strategies. The results suggest the benefits of exploiting available information in selecting seeds.

The third chapter studies the nonparametric identification in generalized separable models. It provides new sufficient conditions for nonparametrically identifying structural models with generalized additive or multiplicative separability. The conditions generalize the normalizations used in the literature, such as differentiability of link and component functions, functional form normalization, and large support of an explanatory variable. We show that when a generalized separable model has two component functions, the identification only requires three location normalizations and weak support conditions. The new results can be applied to various influential econometric models after transformation.