Scott Fay

The Household Bankruptcy Decision

Personal bankruptcy filings have risen from 0.3 percent of households per year in 1984 to around 1.35 percent in 1998 and 1999, transforming bankruptcy from a rare occurrence to a routine event. Lenders lost about

The Role of Marketing in Social Media: How Online Consumer Reviews Evolve

39 billion in 1998 due to personal bankruptcy filings. But economists have little understanding of why households file for bankruptcy or why filings have increased so rapidly. Until very recently, studying the household bankruptcy decision was very difficult, because no household-level data set existed that included information on bankruptcy filings. In this paper, we use new data from the Panel Study of Income Dynamics, which includes information on bankruptcy filings, to estimate a model of households’ bankruptcy decisions. We find support for the strategic model of bankruptcy, which predicts that households are more likely to file when their financial benefit from filing is higher. Our model predicts that an increase of

Probabilistic Goods: A Creative Way of Selling Products and Services

1,000 in households’ financial benefit from bankruptcy would result in a 7-percent increase in the number of bankruptcy filings. Our model also predicts that if the 1997 National Bankruptcy Review Commission’s proposed changes in bankruptcy exemption levels were implemented, there would be a 16-percent increase in the number of bankruptcy filings each year. But if the

Automated strategy searches in an electronic goods market: learning and complex price schedules

100,000 cap on homestead exemptions recently passed by the U.S. Senate were adopted, our model predicts that there would be only a negligible effect on the number of filings. We find little support for the nonstrategic model of bankruptcy which predicts that households file when adverse events occur which reduce their ability to repay. Finally, controlling for state and time fixed effects, our model shows that households are more likely to file for bankruptcy if they live in districts with higher aggregate filing rates.

Implications of Expected Changes in the Seller's Price in Name-Your-Own-Price Auctions

Social media provide an unparalleled platform for consumers to publicize their personal evaluations of purchased products and thus facilitate word-of-mouth communication. This paper examines relationships between consumer posting behavior and marketing variables - such as product price and quality - and explores how these relationships evolve as the Internet and consumer review websites attract more universal acceptance. Based on automobile-model data from several leading online consumer review sources that were collected in 2001 and 2008, this study demonstrates that the relationships between marketing variables and consumer online-posting behavior are different at the early and mature stages of Internet usage. For instance, in the early stage of consumer Internet usage, price is negatively correlated with the propensity to post a review. As consumer Internet usage becomes prevalent, however, the relationship between price and the number of online consumer reviews shifts to a U-shape. In contrast, in the early years, price has a U-shaped relationship with overall consumer rating, but this correlation between price and overall rating becomes less significant in the later period. Such differences at the two different stages of Internet usage can be driven by different groups of consumers with different motivations for online review posting.

Timing of Product Allocation: Using Probabilistic Selling to Enhance Inventory Management

This paper defines a unique type of product or service offering, termed probabilistic goods, and analyzes a novel selling strategy, termed probabilistic selling (PS). A probabilistic good is not a concrete product or service but an offer involving a probability of getting any one of a set of multiple distinct items. Under the probabilistic selling strategy, a multi-item seller creates probabilistic goods using the existing distinct products or services and offers such probabilistic goods as additional purchase choices. The probabilistic selling strategy allows sellers to benefit from introducing a new type of buyer uncertainty, i.e., uncertainty in product assignments. First, introducing such uncertainty enables sellers to create a “virtual” product or service (i.e., probabilistic good), which opens up a creative way to segment a market. We find that the probabilistic selling strategy is a general marketing tool that has the potential to benefit sellers in many different industries. Second, this paper shows that creating buyer uncertainty in product assignments is a new way for sellers to deal with their own market uncertainty. We illustrate two such benefits: (a) offering probabilistic goods can reduce the sellers information disadvantage and lessen the negative effect of demand uncertainty on profit, and (b) offering probabilistic goods can solve the mismatch between capacity and demand and enhance efficiency. Emerging technology is creating exciting (previously unfeasible) opportunities to implement PS and to obtain these many advantages.

Bidding for Bidders? How the Format for Soliciting Supplier Participation in NYOP Auctions Impacts Channel Profit

In an automated market for electronic goods new problems arise that have not been well studied previously. For example, information goods are very flexible. Marginal costs are negligible and nearly limitless bundling and unbundling of these items are possible, in contrast to physical goods. Consequently, producers can offer complex pricing schemes. However, the profit-maximizing design of a complex pricing schedule depends on a producers knowledge of the distribution of consumer preferences for the available information goods. Preferences are private and can only be gradually uncovered through market experience. In this paper we compare dynamic performance across price schedules of varying complexity. We provide the producer with two machine learning methods producer that is performing a naive, knowledge-free form of leanings (function approximation and hill-climbing) which implement a strategy that balances exploitation to maximize current profits against exploration of the profit landscape to improve future profits. We find that the tradeoff between exploitation and exploration is different depending on the learning algorithms employed, and in particular depending on the complexity of the price schedule that if offered. In general, simpler price schedules are more robust and give up less profit during the learning periods even though in our stationary environment learning eventually is complete and the more complex schedules have high long-run profits. These results hold for both learning methods, even though the relative performance of the methods is quite sensitive to choice of initial conditions and differences in the smoothness of the profit landscape for different price schedules. Our results have implications for automated learning and strategic pricing in non-stationary environments, which arise when the consumer population changes, individuals change their preferences, or competing firms change their strategies.

Inferring salesperson capability using stochastic frontier analysis

The sellers threshold price in name-your-own-price auctions varies over time. However, consumers must bid without knowing when these variations occur because the threshold price is unobservable to them. This paper uses an analytical model and laboratory auctions to explore how the frequency of changes in the threshold price impacts consumer bidding behavior in name-your-own-price auctions. In particular, we consider how the frequency of these expected changes affects the optimal pattern of bid sequences (e.g., strictly increasing over time or following a nonmonotonic pattern). We find that when the probability of a price change is moderate, consumers may have an incentive to use nonmonotonic bidding patterns. Rather than steadily increasing their bids over time, consumers will, at some point in the bid sequence, decrease their bid. However, when the expected probability of a price change is very low or very high, consumers do not have an incentive to use nonmonotonic bidding patterns. Interestingly, impatient bidders are more likely to decrease their bids at some point in the bid sequence than patient bidders. Finally, we find that more frequent price changes may increase customer satisfaction.

Partial-Repeat-Bidding in the Name-Your-Own-Price Channel

This paper examines probabilistic selling PS as an inventory-management mechanism, paying special attention to the impact of the timing of product assignment to buyers of probabilistic goods. In practice, sellers tend to offer probabilistic products only after major demand uncertainty has been resolved. By deferring product assignments, a firm is able to obtain more information about demand for each specific product before deciding which product to assign to consumers. However, our analysis demonstrates that PS can be an effective inventory-management mechanism even if the firm allocates products before knowing which product will be more popular and, thus, scarcer. Interestingly, we show that it can be more profitable for the firm to allocate products to consumers before, rather than after, learning the true demand for a product because, although early allocation imposes higher inventory costs as a result of larger required inventory levels, it also enables the firm to charge higher prices. Our results also reveal that, when introducing probabilistic goods, the firm should order less inventory relative to the case where probabilistic goods are not offered if costs are very low but more inventory otherwise. Finally, we show that PS, as an inventory-management mechanism, can create a win--win situation, both improving profit and increasing social welfare. This paper was accepted by J. Miguel Villas-Boas, marketing.

An Empirical Study of the Impact of Nonlinear Shipping and Handling Fees on Purchase Incidence and Expenditure Decisions

In a name-your-own-price (NYOP) auction, consumers bid for a product or service. If a bid exceeds the concealed threshold price, the consumer receives the product at her bid price. This paper examines how to optimize the interactions between the NYOP retailer and service providers, while, at the same time, managing the bid acceptance rates in order to induce the desired consumer bidding behavior. Channel profit is impacted by how the retailer decides whether or not a given consumer bid will be accepted and, if so, which service provider is chosen to supply a unit of the product to the consumer. We devise a mechanism, the modified second-price auction, which maximizes channel profit. This paper was accepted by J. Miguel Villas-Boas, marketing.