1. Substitution between Mass-Produced and High-End Beers Daniel Toro-Gonzalez Ph.D. candidate, School of Economic Sciences (SES) Jill J. McCluskey Visiting Professor, Cornell University and Professor, SES, Washington State University and Ron C. Mittelhammer Regents Professor, SES & Dept. of Statistics Presented at Beeronomics Symposium UC Davis November 3, 2011

2. 2 Macro Brews Dominate many U.S. Markets

3. However, This is Changing Mass producers’ market share still represents the vast majority of sales, but their sales are flat or declining. Trend of consumers switching from mass to craft beers. Consistent with general shift in food preferences:  Increasing desire for variety, taste, and local products.

4. We know that consumers shift from macro to craft brews. Does it go the other way? “…consumers are very loyal to craft beers and not shifting to macro from craft. In economics terms the cross-price elasticity of craft and macro brews appears to be very inelastic, or that beer drinker do not think of macro lagers as a good substitute for micro brews.” - “Beeronomics: Is Craft Beer Recession Proof After All ?”, The Oregon Economics Blog, Thursday, May 7, 2009.

5. Project Objectives beer, differentiated product Estimate demand for beer, which is a differentiated product. Estimate the own-price, cross-price and income elasticities.

6. Data Scanner data from 60 Dominick's supermarkets in Chicago. Seven years of store-level weekly sales data (1991 to 1997) 483UPCs for 343 brands. Product info and store area sociodemographics

7. Market and Product Definition Oligopolistic differentiated product market. Each store is treated as an independent market. Each brand of beer is considered as a product.

8. Types of Beer 1.Mass produced beers 1.Mass produced beers are defined as those with similar characteristics of lightness, same fermentation method (bottom fermenting yeast) and the use of adjuncts such as corn or rice. 2.Import beers 2.Import beers are those produced abroad. craft beers 3.The rest of the beers are called craft beers. 8

9. Number of Firms Long term secular decline in traditional breweries Rapid expansion in specialty breweries since 1980

10. Market Shares by Beer Type Sample Averages for Dominick Stores

11. Discrete Choice Model Issues aggregate Model weekly aggregate sales at each store, by beer type dimensionality Address dimensionality problem (large number of underlying products) by projecting the products onto a characteristics space. differentiated products Market characterized by differentiated products. correlated Prices may be correlated with unobserved demand factors, causing endogeneity problem.

12. Discrete Choice Model

13. Observable Variables  Observed product characteristics: –Size of the bottle –Alcohol content –Type (Mass, Craft, Import) –Style (Ale, Fruit, Low Alcohol, Oktoberfest, Seasonal, Smoked, Steam, Stout, Wheat)  Price  Observed consumer characteristics: –Household income, home value, household size, education (% college graduates), ethnicity (% blacks+hispanics)

14. Discrete Choice Model Linear specification of utility where  j is interpreted as the mean of consumers’ valuations of unobserved product characteristics (product quality). Error term encompasses the distribution of consumer preferences around  j. Errors are i.i.d. with “extreme value” distribution, resulting in a multinomial logit formulation.

15. Mean Utility Representation Simply using  j to represent the mean utility for product j, which is defined as everything other than the error term:

16. Multinomial Logit The market share of product j is then expressible in term of  j :

17. Multinomial Logit Assuming the relationship between observed and predicted market shares is invertible, with the mean utility of the outside good (all other than beers) normalized to zero, Endogeneity Prices and unobserved product attributes are correlated  Endogeneity.

18. Instrument for Prices Prices in other markets? (Hausman, 1996).  Prices of brand j in two markets will be correlated due to the common marginal cost.  But prices in other markets uncorrelated with the market-specific unobserved product characteristics.

19. Variable \ MethodMNLMNL-IV Price-9.10E-06***-0.283*** 0.0000.012 Size9.11E-06***0.054*** 0.000 0.002 Alcohol-2.63E-06***0.029*** 0.0000.010 Craft-1.77E-05***-0.319*** 0.000 0.024 Import-1.74E-05***-0.202*** 0.0000.026 Ethnic8.22E-06 0.139*** 0.000 0.047 Education-2.51E-05 0.217 0.0000.155 Household Size-7.90E-06 -0.179*** 0.000 0.030 Incomes6.85E-08 0.002*** 0.000 Observations12066 R2R2 0.201 0.438 Legend: * p<.1; ** p<.05; *** p<.01. MNL: Ignores endogeneity of prices. MNL-IV: Prices in other markets as IV for Price.

20. Problem with MNL Independence of Irrelevant Alternatives (IIA).  Example, if a consumer wants to try a beer that is an American lager, he/she may consider alternatives like Coors light or Bud Light, but he will not consider any Stout type of beer.

21. Nested Logit Model The NL preserves the assumption that consumer tastes are extreme value distributed. Allows consumer tastes to be correlated across products. More reasonable substitution patterns than in the previous model ( a priori ).

22. Nested Logit Model We divide the products into g different exhaustive and mutually exclusive groups.  is common to all products in group g. averagecorrelation in the random utility across products of the same group (1- σ ) is the average correlation in the random utility across products of the same group.

23. Nested Logit Model Berry (1994) shows that if the errors are i.i.d. extreme value then: it is also distributed as a extreme value.

24. Nested Logit Model We can represent the NL model as: where σ measures average similarity of products within each group of beer types. within group share The new term is the log of the within group share.

25. Variable / MethodMNLMNL-IVNL-IV Price-9.10E-06***-0.283***-0.229*** 0.0000.0120.011 Size9.11E-06***0.054***0.006*** 0.000 0.002 0.001 Alcohol-2.63E-06***0.029***0.060*** 0.0000.0100.008 Craft-1.77E-05***-0.319***-5.253*** 0.000 0.024 0.040 Import-1.74E-05***-0.202***-5.122*** 0.0000.0260.040 Ethnic8.22E-06 0.139***0.090*** 0.000 0.047 0.035 Education-2.51E-05 0.217 -0.130 0.0000.1550.110 Household Size-7.90E-06 -0.179***-0.087*** 0.000 0.030 0.022 Incomes6.85E-08 0.002***0.002*** 0.000 σ(Average across g) 0.892*** 0.000 Observations12066 R2R2 0.201 0.438 0.716 Legend: * p<.1; ** p<.05; *** p<.01.

26. Price Elasticities MassCraftImportOver All Mass-0.12230.00040.0002 Craft0.0028-0.31680.0013 Import0.00040.0008-0.1566 Over All-0.1715 Source: Dominik’s dataset, calculations by the authors.

27. Compare with Other Findings Source: Table 2.2. Tremblay and Tremblay (2005). SourcePrice Elasticity Hogarty and Elzinga 1972-0.889 Orstein and Hanssens 1985-0.142 Tegene 1990-0.768 Lee and Tremblay 1992-0.583 Gallet and List 1998-0.730 Nelson 1999-0.200 Nelson 2003-0.174 This study -0.172

28. Income Elasticities Source: Dominik’s dataset, calculations by the authors. Elasticity Mass0.257 Craft0.434 Import0.460 Over All0.260

29. Price Elasticities: Other Findings Source: Table 2.2. Tremblay and Tremblay (2005). SourceIncome Elasticity Hogarty and Elzinga 19720.430 Orstein and Hanssens 19850.011 Tegene 19900.731 Lee and Tremblay 19920.135 Gallet and List 1998-0.545 Nelson 19990.760 Nelson 2003-0.032 This study 0.260

30. Conclusions Demand for beer is inelastic with respect to prices. Cross-price elasticities are very close to zero.  Mass and craft beers are not close substitutes! From the income elasticities, all of the types of beer (mass, craft, and import) are normal goods.

31. Next Steps Estimate the model using a random coefficients specification for utility. Allow for consumer heterogeneity. Consumer characteristics can interact with product attributes. Examine other formulations/instruments to tackle endogeneity between price and unobserved product characteristics.

32. Thank you and Cheers! Questions? (pictures from the Beeronomics Conference, Belgium May 2009)