Geography and Electronic Commerce: Measuring Convenience, Selection, and Price

1. Geography and Electronic Commerce: Measuring Convenience, Selection, and Price Anindya Ghose (with Chris Forman and Avi Goldfarb) 2007 NET INSTITUTE CONFERENCE

2. Introduction • What are some reasons consumers buy online? • Price • Convenience • Selection/Variety • Each of these components is largely driven by the local availability of offline options

3. What we do • Use the existing theory literature to motivate a set of hypotheses on online/offline channel substitution Use to show how changes in retail supply influence online/offline channel substitution • Examine data from Amazon “purchase circles” • Merge with data on store openings across locations

4. Why might this be interesting? • How do consumers substitute between online and offline channels? • Prior research has focused primarily on effects of price • Little systematic research on other factors affecting consumer purchase behavior in online world. • If you are a online retailer, how does offline entry influence online consumer behavior in you local market? • If you are an offline retailer, what kind of competition do you face online?

5. Hypotheses are motivated by theory literature on spatial competition • Consumers are distributed around unit circle • Incur transportation cost t for visiting offline retailer • Consumers incur disutility cost µ when purchasing online • Choice of channel is determined by location, t, µ, pd and pr Source: Balasubramanian (1998) • Three types of products • Popular products: stocked by all offline retailers • Less popular products: stocked by some offline retailers • Unpopular products: stocked only by online retailer

6. Hypothesis 1: Online purchasing for convenience • H1: As distance to offline stores decreases, the proportion of popular products being bought online also decreases. • Rationale: For popular products, increases in the distance to the closest offline retailer will increase the likelihood that a consumer will purchase from the direct (Internet) retailer, other things equal.

7. Hypothesis 2: Online purchasing for selection • H2: As availability of less popular products in offline markets increases, the proportion of less popular products bought online decreases. • Rationale: As product selection at offline retailers increases, consumers will be able to increasingly purchase less popular products online.

8. Hypothesis 3a & 3b: Online purchasing for price • H3a: As the discounted online price of a commodity falls relative to its list price, consumers are more likely to buy it. This increases the relative online sales of that commodity compared to other online items. • Rationale: Demand curves are downward sloping. • H3b: As distance to offline stores falls, discounted online items will be more likely to be bought offline. • Rationale: Items that are typically discounted online are also discounted offline. Thus, the impact of online discounts will be tempered by the existence of local retail stores with similar discounts.

9. Amazon • Entry Data Data sources • Entry data from Wal-Mart, Target, Barnes and Noble, and Borders • Collected from news wires and from company reports • Amazon Purchase Circle Data • Collected over 10 months from April 2005 to January 2006 • 1501 unique locations in the US Our dependent variable is a binary variable that is equal to one when the book appears among the top 10 books in that location.

12. Data • Product characteristics • Amazon price and list price • Overall Amazon rank (splined) • Results robust to USA Today rankings • Average rating and # of reviews on Amazon • Days since product launch • Store Openings • Discount Store Entry: Wal-Mart and Target (16.4%) • Large Bookstore Entry: Barnes & Noble and Borders (4.6%) • Location and date • Identify places within 5.4 miles of opening • Robust to use of 20 mile radius

13. Descriptive Statistics:There are differences across locations

14. Econometric strategy • Identification: • The product-location fixed effect controls for location-specific preferences for each product • Difference-in-difference: • Relative effect of entry in one location compared to another location • Key idea is that entry will impact the relative popularity of the books purchased online.

16. Results are robust to… • Using distance of 20 miles (rather than 5.4 miles) to define “local” • Defining the total set of books available in a month as 1000 not 300 • Using different breaks in the spline of the overall Amazon sales rank • Using a linear (not splined) definition of the overall Amazon sales rank • Interesting distinctions in subsamples: small/large markets, sales tax/no sales tax

18. What we find: geography matters Goal Findings • Consumers buy different products in different locations • Consumers in different locations use the online channel alternatively to obtain better convenience, selection, and price • Demonstrate that geography matters for electronic commerce • Offline transportation cost, online fit, and prices interact to determine channel choice. • Identified potential extension arising from retailer decisions to stock less popular products. • Provide empirical support for assumptions used in direct marketer circular city model • Demonstrate how convenience matters for electronic commerce • Convenience is the single biggest factor influence online/offline channel substitution in our data

19. Limitations • Only observe the top 10 products in each location. • This limits our ability to make inferences about very rare products. • This is not a “Long Tail” paper. • We lack information on consumer choice of channel. • Our conclusions are about relative popularity, not about quantities sold. • We use “within” market variation over time to make conclusions about “between” market variation

20. Conclusions • Geography matters for electronic commerce • The virtual world improves consumer welfare by easing some constraints of the physical world • BUT, the importance of these constraints varies across locations • We examine convenience, selection, and price. Prior literature has focused on price alone. • Strong evidence of convenience and price effects. • Selection effects only in university towns and large cities and taxable states • We do NOT identify the “long tail”

21. Product Descriptives

22. Descriptives by location