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...

We study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We...

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...

This paper proposes a method of bid-rigging detection, which allows us to reveal cartels in procurement auctions without any prior knowledge of the market structure. We apply it to data on highway construction procurements in one of the Russian regions and show that five suppliers demonstrated...

Many online retailers offer personalized recommendations that help consumers make their choices. While standard recommendation algorithms are designed to guide consumers to the most relevant items, retailers may have strong incentives to deviate from these standard algorithms and instead steer...