Competition and Price Wars in the U. S. Brewing Industry

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Competition and Price Wars in the U.S. Brewing Industry


Jayendra Gokhale and Victor J. Tremblay

Department of Economics

303 Ballard Extension Hall

Oregon State University

Corvallis, Oregon 97331-3612

October 24, 2011
Abstract: The behavior of the macro or mass-producing segment of the U.S. brewing industry appears to be paradoxical. Since Prohibition, the number of independent brewers has continuously declined while the major national brewers such as Anheuser-Busch, Miller, and Coors gained market share. In spite of this decline in the number of competitors, profits and market power remained low in brewing. Iwasaki et al. (2008) explain this result by providing evidence that changes in marketing and production technologies favored larger brewers and forced the industry into a war of attrition where only a handful of firms were destined to survive. This led to fierce price and other forms of competition, especially from the 1970s through the early 1990s. In the last 16 years, the war appears to have subsided. Thus, the purpose of this study is to determine whether price competition has diminished since the mid 1990s. We find weak evidence that competition has diminished but not enough to substantially increase market power.

Keywords: Price Wars, Market Power, Beer Industry
JEL Classification: D22, L11, L66
Acknowledgements: We wish to thank Carol Tremblay for her helpful comments on an earlier version of the paper.

Note: Preliminary draft. Not for quotation without the authors’ permission.

1. Introduction

There are two paradoxical features of the macro or mass-producing segment of the U.S. brewing industry. First, industry concentration has risen steadily since the end of Prohibition. The number of independent macro brewers reached a peak in 1935 at 766 firms and has continuously declined since then to about 20 firms today. This is reflected in the rise in the four-firm concentration ratio (CR4) and the Herfindahl-Hirschman index (HHI), two common measures of industry concentration.1 Figure 1 documents this increase for the period 1947-2009.2 Second, in spite of rising concentration, profits have remained low, and previous studies have failed to detect the presence of market power.3

This appears to be a paradox because many static models of oligopoly suggest that profits and market power will rise with a fall in the number of competitors. Nevertheless, not all models predict this outcome. For example, price equals marginal cost in the Bertrand model when products are homogeneous goods and there are two or more competitors. Furthermore, Tremblay and Tremblay (2011) and Tremblay et al. (forthcoming) demonstrate that price can equal marginal cost even in a monopoly setting when the incumbent firm competes in output and there exists one or more potential entrants that compete in price.

In the brewing industry, Tremblay and Tremblay (2005) speculate that the reason why firm profits remained low is that firms were forced into a generalized war of attrition (Bulow and Klemperer, 1999). In such a war, N = N* + K firms compete in a market that will profitably support only N* firms in the long run. Thus, if K > 0, K firms must exit for the market to reach long-run equilibrium. As documented in Tremblay and Tremblay (2005), two events caused N* to rise in brewing. In the 1950s and 1960s, the advent of television gave a marketing advantage to large national producers who were the only firms large enough to profitably advertise on television.4 In addition, increased mechanization beginning in the 1970s reduced the cost of large scale production. These changes gave a marketing and production advantage to large scale beer producers.

Table 1 shows how the market share of the national beer producer5 grew over time and how changes in marketing and production economies affected optimal firm size. It lists estimates of the minimum efficient scale (MES) needed to take advantage of all scale economies in marketing and production for various years. MES-Output measures annual minimum efficient scale in millions of (31 gallon) barrels. MES-MS measures the market share needed to reach MES-Output. Thus, N* measures the number of firms needed to just produce at MES. This is called the cost-minimizing or efficient industry structure (Baumol et al., 1982). As the table shows, MES grew and N* fell over time.

The intensity of the war is reflected in the number of firms that must exit the industry for the efficient structure to be reached in the long run. It is defined as K = N – N* when (N – N*) > 0 and is 0 otherwise. The value of K was largest in 1960s and 1970s, a period known as the “beer wars”. This is aptly described in Newsweek (September 4, 1978, 60):

After generations of stuffy, family-dominated management, when brewers competed against each other with camaraderie and forbearance, they are now frankly at war. Marketing and advertising, not the art of brewing, are the weapons. Brewers both large and small are racing to locate new consumers and invent new products to suit their taste. Two giants of the industry, Anheuser-Busch of St. Louis and Miller Brewing Company of Milwaukee, are the main contenders.
This description is remarkably accurate, as the facts show that the war was fought with advertising, the introduction of new brands, and tough price competition. Figure 2 plots the advertising intensity of the major brewers, measured as advertising spending per barrel. It shows that advertising was quite high from the mid 1950s through the late 1960s, a period in which television advertising became a prominent tool of the national brewers. In 1950, only 9 percent of households had a television set, a number that increased to 87 percent by 1960 and 95 percent by 1970s.6 Advertising spending rose once again in the 1980s, a period when the Coors Brewing Company made large investments in advertising in order to expand into new regions of the country and become a national brewer.7

Brewers also fought for market share by introducing new brands. Table 2 lists the number of brands offered by the leading brewing. In 1950, most brewers offered a single flagship brand. The Anheuser-Busch Brewing Company is the lone exception, as it had continuously produced a flagship brand, Budweiser, and a super-premium brand, Michelob, since Prohibition. Brand proliferation became apparent by the late 1970s, and by 1990 the major brewers each offered 9 or more different brands of beer.

Iwasaki et al. (2008) formally tested for the effect of the war on concentration and price competition. They found that advertising and rising MES contributed to increases in industry concentration. In spite of rising concentration, they found that the war contributed to low price-cost margins during the 1960s through the early 1990s. Unfortunately, their work does not shed light on the extent to which market power has changed as the war began to wind down by the late 1990s.

There are several reasons why one might expect the intensity of the war to have diminished by the 2000s. First, there is little room left for consolidation. In 2002, Miller was purchased by South African Breweries to form SABMiller. In 2008, Anheuser–Busch was purchased by Belgium’s InBev to form Anheuser–Busch InBev, and Coors and SABMiller established a joint venture called MillerCoors. Second, Pabst gave up the production of beer in 2001, contracting with Miller to produce all of its beer. Finally, the remaining macro brewers have retreated to niche markets, competing more with the micro than the macro brewers.

The purpose of this paper is to determine whether the degree of competition has fallen in the final stages of industry consolidation. Two methods are used. The first is the new empirical industrial organization technique, which uses regression analysis to estimate the markup of price over marginal cost. The second is a new technique that was developed by Boone (2008), which compares the variable profits of efficient with less efficient firms over different regimes of competition. The evidence shows that competition has decreased since 1997 but not enough to substantially increase market power.

2. Estimation of the Degree of Competition

In this section, we review the two methods that are used to estimate the degree of competition in brewing. The first is called the new empirical industrial organization technique.8 The empirical model derives from a general first-order condition of profit maximization. To illustrate, assume a market with N firms, where firm i’s inverse demand is pi(q1, q2, q3, . . . , qN), pi is firm i’s price, and qi is firm i’s output. The firm’s long-run total cost function is C(qi, w), where w is a vector in input prices; marginal cost is MC = ∂C/∂qi. Solving the firm’s first-order condition for price produces an equation called an optimal price equation (supply relation or markup equation):


where θ is a behavioral parameter of market power. We will see subsequently that choosing different values of θ will produce different oligopoly equilibria.

This specification is related to the Lerner (1943) index of market power (). To illustrate, assume that firms produce homogeneous goods, such that pi = p and ∂pi/∂qi = ∂p/∂Q. Under these conditions, Equation (1) can be rearranged as

, (2)

where msi is the market share of firm i, which equals 1/N when the market is in equilibrium because of symmetry. When price equals marginal cost, market power is nonexistent and = 0; increases with market power. This specification describes a variety of possible cooperative and non-cooperative equilibria.

  • In a competitive or Bertrand equilibrium with homogeneous goods, p = MC which implies that θ = 0 and = 0.

  • For a monopolist, θ = N = 1 and = 1/η.

  • In the Cournot equilibrium, θ = 1 and = msi/η = 1/(N·η). Notice that when N = 1, = 1/η which is the simple monopoly outcome.

  • In a perfect cartel, θ = N and = 1/η.

Given that the market outcome will range from competitive to cartel, this implies that 0 ≤ θ ≤ N and 0 ≤ ≤ 1/η. One can think of θ as an indicator of the “toughness of competition,” as described by Sutton (1991).

In its empirical form, Equation (1) is transformed into the following equation.


where is an empirical specification of the marginal cost function and λ = θ(∂p/∂Q) is a market power parameter to be estimated. With appropriate data, Equation (3) is either estimated with firm demand as a system of equations or as a single equation using an instrumental variables technique given that firm output is an endogenous variable. With an estimate of λ and ∂p/∂Q (from the demand function), we can estimate θ as λ/(∂p/∂Q). With this information and the market share of the average firm (or a particular firm), () can be calculated from Equation (2).

The second method that we use to estimate the degree of competition in brewing was developed by Boone (2008). The main advantages of his method are that it requires relatively little data and it avoids the use of accounting cost and profit data, which are poor proxies for their economic counterparts.9 In order to use Boone’s method, two conditions must hold. First, firms must not be equally efficient. This is a reasonable assumption in brewing where some firms have rather antiquated equipment, are unable to advertise nationally, and are scale inefficient. Second, an increase in competition must punish inefficient firms more harshly than efficient firms. This too seems reasonable, because increasingly tougher price competition would be expected to cause the least efficient firms to exit first.

To test for a change in industry competitiveness, one must develop what Boone calls an index of relative profit differences (RPD). RPD compares the variable profits of different firms within an industry. Let (Ei, θ) equal firm i’s variable profit, which is a function of its efficiency level (Ei) and the behavioral parameter (θ). Variable profit equals total revenue minus total variable cost. To illustrate this idea, consider a market with three firms where firm 1 is most efficient and firm 3 is least efficient (E1 > E2 > E3). Recall that θ ranges from 0 (in homogeneous Bertrand) to N (in a cartel), where the degree of competition increases as θ falls. With this notation,

. (4)

Under the conditions of the model, an increase in competition will lead to an increase in RPD, ∂RPD/∂θ < 0. In other words, an increase in competition causes () to increase relative to (). Thus, if RPD rises (falls) over time, we can conclude that competition has increased (decreased) and market power has fallen (risen).

Boone’s index has several desirable qualities. First, by using variable profits, it circumvents the measurement problems associated with accounting profits.10 Second, data are needed for no more than 3 firms in the industry. The only difficulty is that firms must be ranked in terms of their relative efficiency. One approach is to use data envelopment analysis to characterize a firm’s technology and relative inefficiency, as suggested in Färe et al. (1985, 2008). Boone suggests a simple alternative in which the firm with lowest average variable costs is most efficient.

In brewing, previous studies can be used to rank the relative efficiency of firms. In terms of scale efficiency, Tremblay and Tremblay (2005) found that only the industry leader, Anheuser-Busch, has been consistently scale efficient. Since then, Miller has been scale efficient for much of the period, followed by Coors. None of the smaller regional brewers were scale efficient. In terms of marketing efficiency, the advent of television gave an advantage to the large national brewers. This is confirmed by Färe et al. (2004), who found that Anheuser-Busch was the most efficient, while smaller regional brewers and failing firms were the least efficient. Taken as a whole, this implies that the rank order from most to least efficient firms is: Anheuser-Busch, Miller, Coors, and smaller regional brewers.

3. Data and Empirical Results

The data set used in our regression analysis consists of annual observations from 1977 to 2008 for eleven U.S. brewing companies. These include all macro brewers that were publicly owned: Anheuser-Busch, Coors, Falstaff, Genesee, Heileman, Miller, Olympia, Pabst, Pittsburg, Schlitz, and Stroh. Firm variables include price, marginal cost, output, total revenue, and variable profit (total profit minus total variable cost). All firm data derive from the annual trade publication, Beer Industry Update.

The industry data that are used in the study include the measures of industry concentration (HHI and CR4) and a measure of the intensity of the beer wars (WAR). The concentration indices are updated from Tremblay and Tremblay (2005). WAR is defined as N*/N. With this definition, the intensity of the war of attrition increases as WAR decreases.11 The number for firms (N) is updated from Tremblay and Tremblay (2005). The efficient number of firms (N*) equals Q/MES, where industry production (Q) is obtained from Beer Industry Update. An estimate of minimum efficient scale (MES) derives from Tremblay and Tremblay (2005).

We also use two market demand variables that serve as instruments in the optimal price equation. These are per-capita disposable income (1982 dollars) and a demographics variable, the proportion of the population that range in age from 18 to 44. 12 Demand studies show that this is the primary beer drinking age group (see Tremblay and Tremblay, 2005). Table 3 displays the descriptive statistics of the firm, industry, and demand variables.

We first investigate the relative profit differences (RPD). Given data limitations, we analyze the change in RPD for two trios of firms: for Anheuser-Busch, Miller, and Genesee (A-M-G) and for Anheuser-Busch, Coors, and Genesee (A-C-G).13 These data are available from 1978 through 1999. Recall that an increase in RPD implies an increase in competition. The results are reported in Table 4 and are plotted in Figure 3. Consistent with the findings of Iwasaki et al. (2008), the results show that beer industry became more competitive through the mid 1980s, and the degree of competition remained relatively constant during the 1990s.

Next, we perform regression analysis on the optimal price equation (Equation 1). Data limitations require that we use average cost as a proxy for marginal cost. This is a reasonable assumption for the larger national producers, as they will have reached MES. To control for possible cost and other differences between national and regional brewers, we include a dummy variable, DN, which equals 1 for national producers and 0 otherwise.

Given that it is unlikely that market power has remained constant over our sample period, we consider several specifications. As a starting point, we consider the simple model where market power is constant. This model is given by


where β0 and λ are parameters to be estimated. Notice that the parameter on MCi equals 1. In this specification, firms have market power when λ > 0.

This model is unlikely to be valid in brewing, however, given previous evidence that demonstrates that there has been a war of attrition in brewing. One hypothesis is that market power has changed over time and is a function of WAR: λ = β1 + β2WAR. In this case, the model becomes


As we have defined WAR, a reduction in the intensity of the war implies that ∂pi/∂WAR = β2qi > 0. That is, market power increases with the WAR variable.

Tremblay and Tremblay (2005) argue that there were three periods or regimes in brewing that relate to market power. In the first period, 1977-1986, the war was so intense that market power was zero. Market power then rose progressively into the second period (1987-1996) and the third period (1997-present). If this is true, the following model is appropriate.


where q87-96 is firm output during for the second period (1987-1996) and equals 0 otherwise; q87-96 is firm output during the third period and is 0 otherwise. If market power rose from period to period, then β4 > β3 > 0.

In the final specification, we modify Equation (7) to control for the effect of the war on market power during these later regimes. In this case,


This model allows us to determine how market power changed over time and was affected by the WAR variable. In market power has risen over time, then β4 > β3 > 0 and β6 > β5 > 0.

Each of these specifications are estimated, with and without DN, using an instrumental variables estimation technique. As discussed above, the instruments are per-capital disposable income and the population aged 18 to 44. The empirical results are provided in Table 5. The specifications that were estimated are labeled models 1 through 8 (M1- M8) in the table. As the model predicts, the parameter on MC is not significantly different from 1 in each specification (p-value > 0.999). In most specifications, the national dummy variable is positive and significant. The positive sign suggests that national brewers have higher costs, perhaps because they sell a larger share of premium brands and/or have greater market power than regional brewers.14

Regarding the issue of market power, we are particularly interested in two hypotheses. The first is the hypothesis that a decrease in WAR (i.e., an increase in the intensity of the war) reduces market power. This hypothesis is confirmed in models M3 and M4, as the parameter on the interaction variable between output and WAR is positive and significant.

Second, we are interesting in determining whether or not market power has increased progressively from 1987-1996 to 1996-2009. In the absence of the WAR variable, Models M5 and M6 are consistent with this hypothesis. In both models, the parameter estimate on q97-09 is greater than the parameter estimate on q87-96., although the difference between parameters is not significant. We obtain a similar result when we include the WAR variable in models M7 and M8. The parameter estimates on

q97-09 exceeds that of q87-96, and parameter estimates on q97-09·War exceed that of q87-96·War.

To further investigate how market power has changed over time, we estimate the Lerner index for the periods 1987-1996 and 1997-2009 from models M7, and M8. Consistent with Tremblay and Tremblay (2005), the results show that the Lerner index is very close to zero. Nevertheless, even though market power is low, the results show that the Lerner index has risen substantially in the later period, rising by over 500 percent in M7 and over 70 percent in M8. This suggests that even though the war of attrition is drawing to a close, market power has risen only slightly in the U.S. brewing industry.
4. Concluding Remarks

Industry concentration has risen dramatically in the post-World War II era in the macro segment of the U.S. brewing industry. Previous studies show that profits and market power have remained low during the 1970s and 1980s, because firms were forced to compete in a war of attrition. Today, macro beer production is dominated by just two companies, Anheuser-Busch and Miller-Coors. This raises concerns that market power has risen in the last decade. The purpose of this paper is to estimate market power and determine if it has risen in the 1997-2009 period.

Two methods are used to estimate the degree of competition in brewing. The first is the traditional NEIO technique, which we modify to allow market power to vary over time. The second is a technique developed by Boone (2008), which uses data on variable profits to determine whether or not competition has decreased over time. Unfortunately, data limitations make it impossible to use Boone’s method beyond 2001. Nevertheless, regression results from NEIO estimates indicate that market power remains low but has risen in the 1997-2009 period. This suggests that the degree of competition in brewing remains high even though the war of attrition is drawing to a close.


Boone, Jan, “A New Way to Measure Competition,” Economic Journal, 118, August 2008, 1245-1261.

Beer Industry Update, Suffern, NY: Beer Marketer’s Insights, various years.

Bulow, Jeremy, and Paul Klemperer, “The Generalized War of Attrition,” American Economic Review, 89, 1999, 439-468.

Färe, Rolf, Shawna Grosskopf, and C. A. Knox Lovell, The Measurement of Efficiency of Production, New York: Springer, 1985.

__________, __________, __________, Production Frontiers, Cambridge, England: Cambridge University Press, 2008.

Färe, Rolf, Shawna Grosskopf, Barry J. Seldon, and Victor J. Tremblay, “Advertising Efficiency and the Choice of Media Mix: A Case of Beer,” International Journal of Industrial Organization, 22 (4), April 2004, 503-522.

Iwasaki, Natsuko, Barry J. Seldon, and Victor J. Tremblay, “Brewing Wars of Attrition for Profit and Concentration,” Review of Industrial Organization, 33, December 2008, 263-279.

Lerner, Abba P., “The Concept of Monopoly and the Measurement of Monopoly Power,” Review of Economic Studies, 1, June 1934, 157-175.

Steinberg, Cobbertt, TV Facts, New York: Random House, 1980.

Sutton, John, Sunk Costs and Market Structure, Cambridge: MIT Press, 1991.

Tremblay, Carol Horton, and Victor J. Tremblay, “The Cournot-Bertrand Model and the Degree of Product Differentiation,” Economics Letters, 111 (3), June 2011a, 233-235.

__________, and __________, “Recent Economic Developments in the Import and Craft Segments of the U.S. Brewing Industry,” with Carol Tremblay, in Johan Swinnen, editor, The Economics of Beer, Oxford University Press, forthcoming-a.

__________, and __________, New Perspectives on Industrial Organization: Contributions from Behavioral Economics and Game Theory, Springer Publishing, forthcoming-b.

Tremblay, Carol Horton, Mark J. Tremblay, and Victor J. Tremblay, “A General Cournot-Bertrand Model with Homogeneous Goods,” Theoretical Economics Letters, forthcoming-a.

Tremblay, Victor J. and Carol Horton Tremblay, The U.S. Brewing Industry: Data and Economic Analysis, Cambridge: MIT Press, 2005.

Table 1 The Market Share of the National Brewers, Minimum Efficient Scale (MES), the Number of Brewers (N), and the Cost-Minimizing Number of Competitors (N*) in the U.S. Brewing Industry
Year Market Share of MES-Output MES-MS N N* K

National Brewers (Million Barrels) (Percent)


1950 16 0.1 0.1 350 840 0

1960 21 1.0 1.5 175 87 88

1970 45 8.0 6.4 82 16 66

1980 59 16.0 9.0 40 11 29

1990 79 16.0 8.4 29 12 17

2000 89 23.0 14.0 24 7 17

2009 93 23.0 14.0 20 7 13

Notes: MES-Output measures minimum efficient scale measured in millions of (31 gallon) barrels. MES-MS represents the market share needed to reach minimum efficient scale. N* represents the cost-minimizing industry structure (i.e., the number of firms that the industry can support if all firms produce at minimum efficient scale). N is the number of macro brewers. MES-MS ≡ (Industry Output)/MES. N* ≡ 100/MES-MS; rounding errors explain the discrepancy in calculations. K = N – N* when (N – N*) > 0 and equals 0 otherwise.

Sources: Steinberg (1980), the Statistical Abstract of the United States, Tremblay et al. (2005), and Tremblay and Tremblay (2005).
Table 2 Major Domestic Beer Brands of the Anheuser-Busch, Coors, Miller, and Pabst Brewing Companies

Year Anheuser-Busch Coors Miller Pabst

1950 2 1 1 1

1960 4 1 1 9
1970 3 1 4 5
1980 5 2 3 10
1990 10 10 9 17
2000 29 14 21 54
2010 55 - 61* 33

* This reflects the brands for both Miller and Coors, as the companies formed a joint venture in 2008 to form MillerCoors.

Sources: Tremblay and Tremblay (2005) for 1950-2000 and company web pages for 2010.

Table 3 Descriptive Statistics of Firm and Industry Data, U.S. Brewing Industry, 1977-2008

Variable Name




(Std. Dev.)


Firm Variables


Firm output (measured in 10 millions of barrels)






Total revenue (thousands of 1982 dollars)






Price (total revenue divided by output; 1982 dollars per barrel)






Marginal cost (total cost divided by output; 1982 dollars per barrel)






Variable profit (total revenue minus total variable cost; thousands of 1982 dollars)






National Firm Dummy Variable ( = 1 for national producer and 0 otherwise)





Industry Variables


Hirfindahl-Hirschman Index






Four-Firm Concentration Ratio






Efficient number of firms divided by the total number of firms (N*/N)





Demand Variables


Demographic Variable – Proportion of the U.S. population aged 18-44.






Per-capita real disposable income (1982 dollars)





Summary statistics are for the minimum (Min), mean, maximum (Max), and standard deviation (Std. Dev.).

Table 4 Relative Profit Differences (RPD) for Anheuser-Busch, Miller, Coors, and Genesee

Year Anheuser-Busch Anheuser-Busch

Miller Coors

Genesee Genesee

1978 1.46 3.33

1979 1.46 3.21

1980 1.81 3.45

1981 1.28 4.02

1982 2.56 6.15

1983 2.76 4.70

1984 3.56 6.65

1985 3.62 5.12

1986 3.46 5.19

1987 3.98 5.63

1988 4.05 5.11

1989 3.92 5.35

1990 3.87 5.41

1991 3.68 5.79

1992 4.21 5.90

1993 3.62 5.45

1994 3.39 5.12

1995 3.14 5.17

1996 3.76 5.67

1997 3.60 4.98

1998 3.92 4.73

1999 4.25 4.61

Table 5 Parameter Estimates and Standard Errors (in parentheses) of the Optimal Price Equation

Variable M1 M2 M3 M4 M5 M6 M7 M8
MC 1.0388a 1.0374a 1.0466a 1.0474a 1.0689a 1.0474a 1.0723a 1.0630a

(0.009) (0.011) (0.010) (0.010) (0.008) (0.009) (0.008) (0.009)

q 0.9759a 0.2230 -0.4290b -0.3971c

(0.167) (0.229) (0.203) (0.204)

q·War 3.5955a 3.8357a

(0.268) (0.355)

q87-96 0.2781 -0.1484 -0.9443b -0.9243b

(0.335) (0.365) (0.380) (0.380)

q97-09 0.8557a 0.1986 -0.6955a -0.8438a

(0.176) (0.213) (0.245) (0.251)

q87-96·War 2.8567a 2.3587a

(0.397) (0.442)

q97-09·War 4.3037a 3.9444a

(0.453) (0.474)

DN 4.5913a -0.7671 4.6324a 1.9566b

(0.657) (0.75) (0.708) (0.777)

Standard errors are in parentheses. The sample size is 176.

aSignificant at 1 percent.

bSignificant at 5 percent.

cSignificant at 10 percent.
Table 6 Lerner Index Estimates

Time Period Model

M7 M8

1987-1996 0.0021 0.0104

1997-2009 0.0113 0.0179

1 CR4 is defined as the market share of the largest four firms in the industry. HHI is defined as the sum of the squared market shares of all firms in the industry and ranges from 0 to 10,000. To make HHI compatible with CR4, we divide HHI by 100 so that it ranges from 0 to 100.

2 We ignore the craft and import segments of the market. The main reason for this is that most import and craft brands of beer are poor substitutes for regular domestic lager, such as Budweiser, Coors Banquet, and Miller High Life. In addition, when Iwasaki et al. (2008) include this segment as a demand determinant, its effect is never significant. Thus, we focus only on the macro segment of the beer market. See Tremblay and Tremblay (2005, forthcoming-a) for more complete descriptions of the import and micro segment of the U.S. beer industry.

3 For a review of the evidence, see Tremblay and Tremblay (2005).

4 At that time, all television ads were national in scope. No spot or local television advertising was available. This made it too costly for local or regional brewers to advertise on television.

5 For most of the post-World War II era, the major national producers included the Anheuser-Busch, Schlitz, Pabst, Miller, and Coors brewing companies. In the early 1980s, Schlitz went out of business and Pabst played less of a dominant role. Coors became a national brewer in 1991. For further discussion of the evolution of the major brewers, see Tremblay and Tremblay (2005).

6 Today, about 98 percent of households have one or more television sets.

7 Coors reached national status in 1991.

8 For a review of this technique, see Bresnahan (1989). For a discussion of its strengths and weaknesses, see Slade (1995), Genesove and Mullin (1998), Corts (1999), Perloff et al. (2007), and Tremblay and Tremblay (forthcoming-b).

9 For further discussion of this issue, see Fisher and McGowan (1983) and Fisher (1987).

10 That is, one does not need to estimate the appropriate depreciation rate of durable assets that are needed to convert accounting profits to economic profits.

11 This definition makes it easier to interpret the effect of the war on market power in the optimal pricing regression.

12 Income data were obtained from the Bureau of Economic Analysis at Population data were obtained from the U.S. Bureau of the Census at

13 We do not make a comparison of Anheuser-Busch, Miller, and Coors because the relative efficiency of Miller and Coors is frequently too close to call.

14 Notice that in M1, the output variable is positive and significant, implying that all firms have market power during the sample period. This is not true, however, when we include DN in the model.

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