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Category Archives: L4

Pricing and Pack Size Brand, Quantity, and Cost Considerations in Pricing Multipacks of Toothpaste

By Stephanie Wiehe

The US market for toothpaste, like many other goods, is shifting towards selling
in bulk. Multipacks of toothpaste require quantity discounts to incentivize consumers, making buying in bulk a great deal for the savings-minded toothpaste-shopper. It is more difficult to understand, however, producers’ willingness to sell multipacks of toothpaste, when margins are necessarily slimmer than single tubes due to quantity discounts. This paper explores the consumer’s decision in purchasing toothpaste as an interaction between savings on price and inventory considerations, like shopping and carrying costs. My model combines aspects of prior works on second degree price discrimination and quantity discounts with alterations to fit the intricacies of the market for toothpaste. The model’s predictions support the possibility of pack size as a tool for second degree price discrimination as shopping and carrying costs  constitute two markets with different price elasticities of demand for single and multipacks of toothpaste. This work adds to the existing literature on storable goods and non-linear pricing and brings a new economics-based approach to a question faced by toothpaste producers.

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Advisors: Professor Allan Collard-Wexler, | JEL Codes: L11; L42; D4

Investigating a Case of Alleged Collusion in Michigan Public Oil and Gas Lease Auctions

By Lucas Do

The state of Michigan administers oil and gas lease auctions semiannually through the Department of Natural Resources. In June 2012, the international news outlet Reuters published allegations of bid-rigging following the Department’s May 2010 auction. This paper empirically investigates the validity of Reuters’ allegations by analyzing auction bid sheets from 2008 to 2018 as well as other data reflecting market conditions over time. To this end, I first formulate a benchmark structural model of bidders’ valuations and estimate it with auction data from a period during which I assume no collusion occurred. Then, I extend the benchmark model by endogenizing bidders’ decision to collude. Using the extended model and the estimated benchmark parameters, I apply the simulated method of moments to solve for the collusive probability that “best” explains the observed bids during the alleged period of collusion. After discovering strong evidence for bid-rigging, I run counterfactual simulations to estimate the revenue damage caused to the state of Michigan by this non-competitive bidding behavior. I find that the hypothetical revenue damage, summed over the entire alleged collusive period, totals over $450 million. However, although these findings lend support to Reuters’ allegations and are contrary to the Department of Justice’s conclusion in 2014 after they had probed the case, they should be approached only with caution, given the limitations of the available data on the potential bidders.

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Advisors: Professor Jame Roberts, Professor Michelle Connolly | JEL Codes: L4, D44, L71

The Determination of Newspaper Slant in Small Markets

By Jordyn Gracey

This paper takes the assertion, made by Gentzkow et al., that newspaper slant is primarily determined by slant as given. Both that paper and this one use Hotelling as a foundation. However, this paper considers what happens when the distribution of ideological preferences differs at national and county levels. This paper controls for the size of the market in which the newspapers are operating as well as for the make-­‐up of the county-­‐level population. Findings show that demand is a robust determinant of slant across market sizes and that supply-­‐side factors rarely have significant impact on slant. In the two cases where ownership does have an effect on slant, it is in regressions where the largest-­‐circulating newspapers have been dropped. We determine that if ownership is important it is when control is more centralized,if a newspaper is operating in a small market or if the owner chooses the slant before deciding which market to enter.

Honor’s Thesis

Data Set

Advisor: Michelle Connolly | JEL Codes: L2, L21, L4, L22, L25, L44, Y8 | Tagged: Firm Behavior, Conglomerates, Product Strategy

Questions?

Undergraduate Program Assistant
Jennifer Becker
dus_asst@econ.duke.edu

Director of the Honors Program
Michelle P. Connolly
michelle.connolly@duke.edu