Network Effects in Alternative Fuel Adoption: Empirical Analysis of the Market for Ethanol

This paper investigates the importance of network effects in the demand for ethanol-compatible vehicles and the supply of ethanol fuel retailers. An indirect network effect, or positive feedback loop, arises in this context due to spatially-dependent complementarities in the availability of ethanol fuel and the installed base of ethanol-compatible vehicles. Marketers and social planners are interested in whether these effects exist, and if so, how policy might accelerate adoption of the ethanol fuel standard within a targeted population. To measure these feedback effects, I develop an econometric framework that considers the simultaneous determination of ethanol-compatible vehicle demand and ethanol fuel supply in local markets. The demand-side of the model considers the automobile purchase decisions of consumers and fleet operators, and the supply-side model considers the ethanol market entry decisions of competing fuel retailers. I propose new estimators that address the endogeneity induced by the co-determination of alternative fuel vehicle demand and alternative fuel supply. I estimate the model using zip code level panel data from six states over a six year period. I find the network effect to be highly significant, both statistically and economically. Under typical market conditions, entry of an additional ethanol fuel retailer leads to a 12% increase in consumer demand for ethanol compatible vehicles. The entry model estimates imply that a monopolist requires a local installed base of at least 204 ethanol-compatible vehicles to be profitable. As an application, I demonstrate how the model estimates can inform the promotional strategy of a vehicle manufacturer. Counter- factual simulations indicate that subsidizing fuel retailers to offer ethanol can be an effective policy to indirectly increase ethanol-compatible vehicle sales.

[1]  Marc Rysman,et al.  Competition between Networks: A Study of the Market for Yellow Pages , 2002 .

[2]  M. Arellano,et al.  Another look at the instrumental variable estimation of error-components models , 1995 .

[3]  Mark Billinghurst,et al.  Crossing the Chasm , 2001 .

[4]  John J. Gart,et al.  The bias and higher cumulants of the logarithm of a binomial variate , 1986 .

[5]  Harikesh S. Nair,et al.  Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants , 2004 .

[6]  Pradeep K. Chintagunta,et al.  Dynamic Standards Competition and Tipping: The Case of 32/64 Bit Video Game Consoles , 2007 .

[7]  John D. Benjamin,et al.  The Evolution of Shopping Center Research: A Review and Analysis , 1994 .

[8]  Jeffrey M. Woodbridge Econometric Analysis of Cross Section and Panel Data , 2002 .

[9]  L. Hansen LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF , 1982 .

[10]  C. Johnson,et al.  E85 Retail Business Case: When and Why to Sell E85 , 2007 .

[11]  Steven T. Berry Estimation of a Model of Entry in the Airline Industry , 1992 .

[12]  James J. Heckman,et al.  Handbook of Econometrics , 1985 .

[13]  Steven T. Berry,et al.  Empirical Models of Entry and Market Structure , 2007 .

[14]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[15]  Timothy F. Bresnahan,et al.  Entry and Competition in Concentrated Markets , 1991, Journal of Political Economy.

[16]  Michaela Draganska,et al.  The Impact of Quality and Variety on Product Assortment Decisions: An Empirical Investigation , 2003 .

[17]  Joel Waldfogel,et al.  Free Entry and Social Inefficiency in Radio Broadcasting , 1996 .

[18]  Pablo T. Spiller,et al.  Competition and Entry in Small Airline Markets , 1989, The Journal of Law and Economics.

[19]  Building out alternative fuel retail infrastructure: Government fleet spillovers in E85 , 2009 .

[20]  Joseph Farrell,et al.  Standardization, Compatibility, and Innovation , 1985 .

[21]  Hongju Liu,et al.  Dynamics of Pricing in the Video Game Console Market: Skimming or Penetration? , 2010 .

[22]  H. Ohashi The Role of Network Effects in the US VCR Market, 1978-1986 , 2003 .

[23]  Neil Gandal,et al.  The Dynamics of Technological Adoption in Hardware/Software Systems , 1997 .

[24]  Amiya K. Basu,et al.  Indirect Network Externality Effects on Product Attributes , 2003 .

[25]  Stacy Cagle Davis,et al.  Transportation Energy Data Book: Edition 25 , 2006 .

[26]  Steven T. Berry Estimating Discrete-Choice Models of Product Differentiation , 1994 .

[27]  Sangin Park Quantitative Analysis of Network Externalities in Competing Technologies: The VCR Case , 2004, Review of Economics and Statistics.

[28]  Neil Gandal,et al.  NETWORK EFFECTS, SOFTWARE PROVISION, AND STANDARDIZATION* , 1992 .

[29]  B. Caillaud,et al.  Chicken & Egg: Competition Among Intermediation Service Providers , 2003 .

[30]  Maria Ana Vitorino Empirical Entry Games with Complementarities: An Application to the Shopping Center Industry , 2011 .

[31]  C. Shapiro,et al.  Technology Adoption in the Presence of Network Externalities , 1986, Journal of Political Economy.

[32]  D. Jain,et al.  Modeling the Evolution of Markets with Indirect Network Externalities: An Application to Digital Television , 1999 .

[33]  Steven T. Berry,et al.  Automobile Prices in Market Equilibrium , 1995 .

[34]  Soren T. Anderson,et al.  Using Loopholes to Reveal the Marginal Cost of Regulation: The Case of Fuel-Economy Standards , 2011 .

[35]  Paul B. Ellickson,et al.  Enriching Interactions: Incorporating Revenue and Cost Data into Static Discrete Games , 2007 .

[36]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .