Do research joint ventures serve a collusive function?

Every year thousands of firms are engaged in research joint ventures (RJV), where all knowledge gained through R&D is shared among members. Most of the empirical literature assumes members are non-cooperative in the product market. But many RJV members are rivals leaving open the possibility that firms may form RJVs to facilitate collusion. We examine this by exploiting variation in RJV formation generated by a policy change that affects the collusive benefits but not the research synergies associated with a RJV. We use data on RJVs formed between 1986 and 2001 together with firm-level information from Compustat to estimate a RJV participation equation. After correcting for the endogeneity of R&D and controlling for RJV characteristics and firm attributes, we find the decision to join is impacted by the policy change. We also find the magnitude is significant: the policy change resulted in an average drop in the probability of joining a RJV of 34% among telecommunications firms, 33% among computer and semiconductor manufacturers, and 27% among petroleum refining firms. Our results are consistent with research joint ventures serving a collusive function

Cooperation in the Classroom: Experimenting with Research Joint Ventures

This paper describes a classroom exercise that illustrates the investment incentives facing firms when technological spillovers are present. The game involves two stages in which student “sellers” first make investment decisions then production decisions. The classroom game can be used to motivate discussions of research joint ventures, the free-rider problem, collusion, and antitrust policy regarding research and development.

Advertising in the US Personal Computer Industry

Traditional models of consumer choice assume consumers are aware of all products for sale.This assumption is questionable, especially when applied to markets characterized by a high degree of change, such as the personal computer (PC) industry. I present an empirical discrete-choice model of limited information on the part of consumers, where advertising influences the set of products from which consumers choose to purchase. Multi-product firms choose prices and advertising in each medium to maximize their profits. I apply the model to the US PC market, in which advertising expenditures are over $2 billion annually. The estimation technique incorporates macro and micro data from three sources. Estimated median industry markups are 19% over production costs. The high industry markups are explained in part by the fact that consumers know only some of the products for sale.Indeed estimates from traditional consumer choice models predict median markups of one fourth this magnitude. I find that product-specific demand curves are biased towards being too elastic under traditional models of consumer choice. The estimates suggest that PC firms use advertising media to target high-income households, that there are returns to scope in group advertising, and that word-of-mouth or experience plays a role in informing consumers. The top firms engage in higher than average advertising and earn higher than average markups.

Marijuana on Main Street: What if?

Illicit drug use is prevalent around the world. While the nature of the market makes it difficult to determine the total sales worldwide with certainty, estimates suggest sales are around 150 billion dollar a year in the United States alone. Among illicit drugs marijuana is the most commonly used, where the US government spends upwards of $7.7 billion per year in enforcement of the laws for marijuana sales (Miron, 2005). For the past 30 years there has been a debate regarding whether marijuana should be legalized. There are two important avenues through which legalization could impact use: legalization would make marijuana easier to get, and it would remove the stigma (and cost) associated with illegal behavior. Studies to date have not disentangled the impact of limited accessibility from consumption decisions based solely on preferences. However, this distinction is particularly important in the market for cannabis as legalizing the drug would impact accessibility. Hence, if most individuals do not use because they don't know where to buy it, but would otherwise use, we would see a large increase in consumption ceteris paribus, which would be important to consider for policy. On the other hand, if accessibility plays little role in consumption decisions, then making drugs more readily available would impact the supply more. In order to access the impact of legalization on use, it is necessary to explicitly consider the role played by accessibility in use, the impact of illegal actions in utility, as well as the impact on the supply side. In this paper, we develop and estimate a model of buyer behavior that explicitly considers the impact of illegal behavior on utility as well as the impact of limited accessibility (either knowing where to buy or being offered) an illicit drug on using the drug. We use the demand side estimates to conduct counterfactuals on how use would change under a policy of legalization. We conduct counterfactuals under different assumptions regarding how legalization would impact the supply as well as various tax policies on the price of cannabis

Limited Information and Advertising in the U.S. Personal Computer Industry

Traditional discrete-choice models assume buyers are aware of all products for sale. In markets where products change rapidly, the full information assumption is untenable. I present a discrete-choice model of limited consumer information, where advertising influences the set of products from which consumers choose to purchase. I apply the model to the U.S. personal computer market where top firms spend over $2 billion annually on advertising. I find estimated markups of 19% over production costs, where top firms advertise more than average and earn higher than average markups. High markups are explained to a large extent by informational asymmetries across consumers, where full information models predict markups of one-fourth the magnitude. I find that estimated product demand curves are biased toward being too elastic under traditional models. I show how to use data on media exposure to improve estimated price elasticities in the absence of micro ad data. Copyright 2008 The Econometric Society.