Bias from ignoring price dispersion in demand estimation

Consumers often pay different prices for the same product bought in the same store at the same time. However, the demand estimation literature has ignored that fact using, instead, aggregate measures such as the “list” or average price. In this paper we show that this will lead to biased price coefficients. Furthermore, we perform simple comparative statics simulation exercises for the logit and random coefficient models. In the “list” price case we find that the bias is larger when discounts are higher, proportion of consumers facing discount prices is higher and when consumers are more unwilling to buy the product so that they almost only do it when facing discount. In the average price case we find that the bias is larger when discounts are higher, proportion of consumers that have access to discount are similar to the ones that do not have access and when consumers willingness to buy is very dependent on idiosyncratic shocks. Also bias is less problematic in the average price case in markets with a lot of bargain deals, so that prices are as good as individual. We conclude by proposing ways that the econometrician can reduce this bias using different information that he may have available.

Exclusivity contracts and competition : the case of the Brazilian fuels market

Exclusivity contracts can help stations by providing brand-value that allows them to obtain higher profits, relative to unbranded retailers. However, branded retailers may have a stronger negative effect over its competitors’ profits. It is not clear which one of these two effects dominates (brand-value vs competition effect). Therefore, the impact of exclusivity over the number of participants in the downstream market is not determined. In this paper, I empirically study the effects of exclusivity agreements on competition in the Brazilian gasoline sector. In order to do so, I estimate an entry model of endogenous product-type choices using data of retailers’ locations and contract choices along with data from the 2010 Brazilian Census. I use my estimates to simulate entry decisions under two counterfactual scenarios: i) mandatory exclusivity and ii) no exclusivity.

Learning in peer-to-peer markets: evidence from Airbnb

Peer-to-peer markets are highly uncertain environments due to the constant presence of shocks. As a consequence, sellers have to constantly adjust to these shocks. Dynamic Pricing is hard, especially for non-professional sellers. We study it in an accommodation rental marketplace, Airbnb. With scraped data from its website, we: 1) describe pricing patterns consistent with learning; 2) estimate a demand model and use it to simulate a dynamic pricing model. We simulate it under three scenarios: a) with learning; b) without learning; c) with full information. We have found that information is an important feature concerning rental markets. Furthermore, we have found that learning is important for hosts to improve their profits.