We study the effect of internet diffusion on childbirth procedures performed in England between 2000 and 2011. We exploit an identification strategy based on geographical discontinuities in internet access generated by technological factors. We show that broadband internet access increased...

We examine the effect of internet diffusion on the uptake of an important public health intervention: the measles, mumps and rubella (MMR) vaccine. We study England between 2000 and 2011 when internet diffusion spread rapidly and there was a high profile medical article (falsely) linking the MMR...

We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit...

Researchers are increasingly able to observe consumers’ behavior prior to a purchase, such as their navigation through a store or website and the products they consider. Such pre-purchase (or search) data can be valuable to researchers in a variety of ways: as an additional source of...

We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

We provide a detailed overview of the empirical implementation of the sequential search model proposed by Weitzman (1979). We discuss the assumptions underlying the model, the identifica-tion of search cost and preference parameters, the necessary normalizations of utility parameters,...

We analyze structural state dependence in brand choice using variation from brand switching during stock-outs caused by hurricanes. We derive a simple test for structural state dependence based on the time-series of choice persistence for households affected by the stock-outs. Using data from...

We quantify the prevalence of undisclosed influencer posts on Twitter across a large set of brands based on a unique data set of over 100 million posts. We develop a novel method to detect undisclosed influencer posts and find that 96% of influencer posts are not disclosed as such. Despite...

We study the estimation of causal treatment effects on demand when treatment is randomly assigned but prices adjust in response to treatment. We show that regressions of demand on treatment or on treatment and price lead to biased estimates of the direct treatment effect. The bias in both cases...