Quantitative Marketing and Economics最新文献

What can we learn from auction data when the seller submits shill bids to inflate the auction price? I study identification in an incomplete model of an English auction with shill bidding in the context of independent private values. I show that the distribution of valuations is partially identified (as is the optimal reserve price), and I provide bounds for the distribution of valuations that hold even when the seller is not engaging in shill bidding. I apply these results to a sample of eBay auctions.

We analyze a large-scale randomized field experiment in which a search engine varied the prominence of search ads for 3.3 million US users: one group of users saw the status quo, while the other saw a lower level of advertising (with search ads sidelined). While lowering advertising significantly decreases the search engine’s revenue as expected, users exposed to the decreased level of advertising also decrease their overall search engine usage. This reduction is more significant among multi-homing users. On the supply side, going from the status quo to lower level of advertising decreases traffic to newer websites, with the newest decile losing traffic by 10%. Overall, our data suggest that viewing search ads makes consumers better off at the margin we study. We illustrate a constructive role of search advertising where advertising fills significant information gaps by conveying new information that is difficult for the search engines to gather and therefore missed by their organic algorithms.

Digital platforms sometimes offer incentives to a subset of sellers to nudge behavior, possibly affecting the behavior of all sellers in the equilibrium. In this paper, we study a policy change on a large e-commerce platform that offers financial incentives only to platform-certified sellers when they provide fast handling and generous return policies on their listings. We find that both targeted and non-targeted sellers become more likely to adopt the promoted behavior after the policy change. Exploiting a large number of markets on the platform, we find that in markets with a larger proportion of the targeted population—hence more affected by the policy change—non-targeted sellers are more likely to adopt the promoted behavior and experience a larger increase in sales and equilibrium prices. This finding is consistent with our key insight that a targeted incentive may increase demand for non-targeted sellers when both platform certificates and the promoted behaviors are quality signals. Our results have implications for digital platforms that use targeted incentives.

Consumer demand in a marketplace is often characterized to be multiple discrete in that discrete units of multiple products are chosen together. This paper develops a sequential choice model for such demand and its estimation technique. Given an inherently high-dimensional problem to solve, a consumer is assumed to simplify it to a sequence of one-unit choices, which eventually leads to a shopping basket of multiple discreteness. Our model and its estimation method are flexible enough to be extended to various contexts such as complementary demand, non-linear pricing, and multiple constraints. The sequential choice process generally finds an optimal solution of a convex problem (e.g., maximizing a concave utility function over a convex feasible set), while it might result in a sub-optimal solution for a non-convex problem. Therefore, in case of a convex optimization problem, the proposed model can be viewed as an econometrician’s means for establishing the optimality of observed demand, offering a practical estimation algorithm for discrete optimization models of consumer demand. We demonstrate the strengths of our model in a variety of simulation studies and an empirical application to consumer panel data of yogurt purchase.

We explore the relationship between the content of political advertising on television and ad effectiveness. Specifically, we investigate how slant – the extremeness of the message – and consistency with the candidate’s primary campaign messaging in national ad buys relate to two measures of voter behavior: online word-of-mouth (WOM) and voter preference (captured through daily polls) for the candidates. Using data from the 2016 presidential election, we find that ad messages that are more (1) centrist and (2) consistent with a candidate’s primary-election platform associate with increases in online WOM and voter preference for the candidate. We further find that consistency is more important in the early (pre-October) stages of the campaign. Our results suggest that while there may be a benefit to candidates moderating their message after winning the primary election, they need to be careful about shedding their messaging from the primary election during the early stages of the general election. Additionally, our results enrich our understanding of the use of extreme messaging in political advertising, a phenomenon that is on the rise, by showing that it may have a cost of decreased candidate-related WOM and voter preference for the candidate.

We provide generalizable results on the price and promotion tactics employed in the U.S. retail grocery industry. First, we document a large degree of price dispersion for UPCs and brands across stores, both nationally and at the local market level. Base price differences across stores and price promotions contribute to the overall price variance, and we show how to decompose the price variance into base price and promotion components. Second, we document that a large percentage of the variation in prices and promotion tactics across stores can be explained by retail chain and especially market/chain factors, whereas market factors explain only smaller percentage of the variation. Third, we show that the chain-level price and promotions similarity can be explained by similarity in demand. In particular, a large percentage of the variance in price elasticities and promotion effects can be explained by retail chain and especially market/retail chain factors. Further, price elasticities and promotion effects across stores of the same chain are hard to distinguish from the chain-market-level mean, and cross-price elasticities are typically imprecisely estimated. These findings suggest that retail managers may plausibly consider price discrimination across stores to be infeasible.

Prices often react stronger to rising than to falling costs. This asymmetric cost pass-through is still not fully understood, but recent theories suggest that asymmetric adjustments of consumers’ search efforts to rising and to falling prices may be one explanation for this pattern. I use novel panel data to investigate the interaction of consumer search intensity, pricing and cost pass-through of residential electricity tariffs on online price comparison sites. I find that consumers search slightly more when prices rise but drastically decrease search efforts when they fall. Moreover, I find direct evidence that cost pass-through heavily depends on consumers’ search efforts in that cost increases are passed-through less to the consumer when search intensity is high while cost decreases are passed-through more when search intensity is high. This finding may help upstream firms to better understand how their price changes will translate into retail price adjustments.

In this paper, I study the short-run effect of social distancing due to the COVID-19 outbreak on movie demand and box-office revenue. Using longitudinal data on the Korean movie theater industry, I first estimate a nested logit model of movie demand, and then quantify the revenue loss in the industry. Estimation results reveal that the revenue loss due to the decrease in underlying movie demand is approximately 52 million dollars nationwide during the first five weeks after the outbreak, implying a 34 percent decrease in sales. The results also suggest an additional 42 million dollars were lost as the delay of some major movies lowered the overall quality of available movies in the market.