Olivia R. Natan Publications

Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subunits. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision makers.

We propose a demand estimation method that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

We propose a demand estimation method that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand.

Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subordinates. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is inconsistent with canonical dynamic pricing models. However, we show that observed prices can be rationalized as an equilibrium of a game played by departments who each have decision rights for different inputs that are supplied to the observed pricing heuristic. Incorrectly assuming that the firm solves a standard profit maximization problem as a single entity understates overall welfare actually achieved but affects business and leisure consumers differently. Likewise, we show that assuming prices are set through standard profit maximization leads to incorrect inferences about consumer demand elasticities and thus welfare.

We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We find considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This amplifies cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect.

Firms often involve multiple departments for critical decisions that may result in coordination failures. Using data from a large U.S. airline, we document the presence of important pricing biases that differ significantly from dynamically optimal profit maximization. However, these biases can be rationalized as a “second-best” after accounting for department decision rights. We show that assuming prices are generated through profit maximization biases demand estimates and that second-best prices can persist, even under improvements to pricing algorithm inputs. Our results suggest caution in abstracting from organizational structure and drawing inferences from firms’ pricing decisions alone.

Although typically modeled as a centralized firm decision, pricing often involves

multiple organizational teams that have decision rights over specific pricing inputs.

We study team input decisions using comprehensive data from a large U.S. airline.

We document that pricing at a sophisticated firm is subject to miscoordination across

teams, uses persistently biased forecasts, and does not account for cross-price elasticities.

With structural demand estimates derived from sales and search data, we find

that addressing one team’s biases in isolation has little impact on market outcomes.

We show that teams do not optimally account for biases introduced by other teams.

We estimate that corrected and coordinated inputs would lead to a significant reallocation

of capacity. Leisure consumers would benefit from lower fares, and business

customers would face significantly higher fares. Dead-weight loss would increase in

the markets studied. Finally, we discuss likely mechanisms for the observed pricing

biases.

We study how organizational boundaries affect pricing decisions using comprehensive data from a large U.S. airline. We document that the firm’s advanced pricing algorithm, utilizing inputs from different organizational teams, is subject to multiple biases. To quantify the impacts of these biases, we estimate a structural demand model using sales and search data. We recover the demand curves the firm believes it faces using forecasting data. In counterfactuals, we show that correcting biases introduced by organizational teams individually have little impact on market outcomes, but coordinating organizational outcomes leads to higher prices/revenues and increased deadweight loss in the markets studied.