Domestic beer industry forecast and trends

7 December 2016

Team 13Jeremy Heffner

Wiebke HeinrichWilliam LindbergRyan McCluskey

Sherry Zhang

I. Industry Trends & Market Composition

The American beer industry over the past 26 years has seen profound changes, morphing from a competitive market with many players to a concentrated industry that can be best described in four words: fight for market share.

The American brewing industry is facing a mature domestic market with stagnating demand and modest growth in revenues (Freedonia, February 2016). A trend that is evident looking at the sales and pricing data of the past 26 years, which shows declining sales growth and decreasing prices (Exhibit 1).

One explanation for the declining sales is a change in consumer demand. Market analysis by the Beverage Information Group highlights that beer accounted for 60% of alcohol servings consumed in 2002, but diminished to 51% in 2015 as consumers increasingly turned to wine and spirits (Liberum, October 2016). This sentiment that was reflected in the survey we conducted, where only 46% of our 188 respondents rated beer as their alcoholic beverage of choice. This trend is anticipated to continue as millennials, a coveted target market representing more than one quarter of the nation’s population (Census Bureau Reports, 2015), seek greater variety and show preference towards wine and spirits (Brager & Greco, 2011). In addition, the rise in popularity of craft breweries since the 1980’s has increased regional competition and forced companies to consolidate through mergers and acquisitions in an effort to stay competitive in the overall stagnant industry.

Currently, Anheuser-Bush InBev (AB-InBev) and SABMiller clearly dominate the domestic beer market as an oligopoly. Evidence for this can be seen in the current number of domestic producers and their respective production volume. While the US industry was comprised over 3,500 brewers in 2015, US sales were dominated by two large scale brewers - AB InBev and

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Exhibit 1.

MillerCoors – who collectively supplied about 75% of beer consumed in 2015 (Freedonia,February 2016).

Both AB-InBev and MillerCoors are key market players and try to differentiate their products through advertising and branding. While this creates some consumer loyalty, and their products can serve as substitutes for one another, AB-InBev holds a dominant market position with 45% of market share (Anheuser-Busch InBev, n.d.). Thus, the domestic beer market can be best described as a Stackelberg oligopoly. Both companies have fought bitter price wars in the past (Peltz, 1997) and invested in large advertising campaigns aimed at increasing market share. For example, between 1992-2014, Miller Lite went through 11 different can designs (Kell, 2015) in the attempt to rebrand itself and attract more customers, which ultimately failed.

While both AB-InBev and SABMiller have faced intense competition from each other, both have managed to thrive historically. However, the positon of both companies has come under increasing pressure through continued market entry by small, independent craft breweries, which increases industry competition and threatens market share and profit margins for both companies. Given the potential threat to both AB-InBev and SABMiller the recent merger announcement shows that both companies see a need to position themselves in a way that will allow them to maintain their market advantages and dominant position within the globally and domestic beer industry in the future.

II. Multiple Variable Regression

Demand Variables

To better understand what drives demand for beer, we conducted primary research by collecting consumption data from 188 individuals through an online survey, as well as secondary research through industry reports and articles. Leveraging both methodologies, we identified 8 key beer consumption indicators, spanning the socioeconomic, demographic, and consumer behavior categories.

Price – A recent study in the Atlantic Economic Journal found that, while beer demand historically had been inelastic with little consumer price sensitivity, new research shows that consumers are more willing to substitute beer with wine and thus beer prices have become more elastic (Fleissig, 2016). We think this finding, along with the strong responses of our survey respondents, of whom 73% stated price as an important purchasing factor for beer, implies that beer demand is sensitive to price changes. We therefore expect a negative correlation between price and beer demand.

Employment Status – Although our survey results did not indicate a strong relationship between employment status and beer demand, industry research considers unemployment rate a factor influencing demand (Freedonia, February 2016). We also believe that changes in the unemployment rate will impact spending behavior and therefore expect negative correlation unemployment rate and demand for.

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Personal Spending - Although we did not address personal spending through our survey, several industry studies reference personal spending patterns as a key economic indicator for beer consumption (Freedonia, February 2016). Given the importance of spending power, we expect overall consumer spending will have a positive correlation to demand.

Gender – Men consume 80% of the beer volume (Goldammer, 2008) which is consistent with our survey that showed male respondents predominantly rating beer as their preferred alcoholic beverage versus women who preferred wine. Because beer consumption is so heavily driven by men, we expect a negative correlation between wine and spirit consumption rates among men and beer demand.

Age – Our survey found that 50% of respondents aged 18 to 24, and 57% of respondents age 25-34, drink beer at least once a week, a finding that is consistent with a recent marketing study which identified the 18 to 34 age group as the “core beer audience” (Mintel, 2015). Given the importance of young consumers coupled with an increase in the aging American population (Mather, Pollard, & Jacobse, 2016) we expect a negative correlation between the increasing age demographics of Americans and beer demand.

Life Stage – Like age, demographic groups such as young couples and bachelors were the main beer consumer group in our survey. We expect that any shift in the population distribution to more mature life stages (empty nesters) will negatively correlate with beer demand.

Geographic Location – Our survey had a limited geographic reach and we therefore were not able to identify geographic difference in beer consumption. However, the National Institute on Alcohol Abuse and Alcoholism depicts differences in geographic alcohol consumption (NationalInstitute on Alcohol Abuse and Alcoholism, 2016) and we expect a positive correlation for regions with high overall alcohol consumption and beer demand.

Beverage Preference – Industry research has found that changing consumer preferences have led to a greater substitution from beer to wine and spirits (Liberum, October 2016). A trend reflected in our survey, where 46% of respondents ranked beer as their alcoholic beverage of choice. We expect consumption of wine or spirits will be negatively correlated to beer demand.

To test our hypothesis, we collected annualized data sets from various authoritative sources including the U.S. Census Bureau, the Bureau of Labor Statistics, and the National Institute on Alcohol Abuse and Alcoholism to determine if there is a correlation between our variables and beer demand. Please see Appendix I for a complete list of raw data sources.

Demand Equation

To ensure a valid model we used data sets that provided unique information to reduce the risk of multicollinearity. We further focused on data sets that covered the entire 1990 to 2015 time period to avoid estimation of data which could compromise model accuracy. One exception is the 2015 annual alcohol consumption per capita value that was extrapolated due to the study period ending in 2014 (National Institute on Alcohol Abuse and Alcoholism, 2016). We estimated the 2015 value by calculating the average 5-year change rate and multiplied the rate by the 2014 value.

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In analyzing our datasets, we found that beer demand in thousands of barrels (Q) can be predicted by the following independent variables: real price of beer (per barrel), annual unemployment rate, annual personal savings rate, and percent change in per capita consumption of all alcoholic beverages. These variables were selected because our regression analysis showed that all variables where statistically significant and produced a reliable model.

The below equation highlights the relationship between the four independent variables and beer demand:

Q US Beer Sales = 247644 – 905 Real Price per barrel + 1772 Unemployment Rate Annual – 1651 Personal Savings Rate + 793 % Change in Alcohol Consumption

The equation shows that both price and personal savings rate are inversely related to beer demand, highlighting that a reduction in either variable will increase demand.

For example: holding all other variables constant, a $1 price decrease will increase Q Beer Sales by 905,000 barrels. A 1% savings rate reduction will increase Q Beer Sales by 1,651,000 barrels.

In contrast, unemployment rate and percent change in per capita alcohol consumption have a positive relationship to demand. Demand will increase as either variable increases and demand will decrease when either variable decreases.

For example: holding all other variables constant, a 1% increase in unemployment rate will increase Q Beer Sales by 1,772,000 barrels, while a 1% increase in per capita alcohol consumption will increase Q Beer Sales by 793,000 barrels.

Looking at our overall demand equation, we conclude that economic drivers as well as consumer preferences are critical in forecasting future beer demand.

Regression Analysis

The above demand equation was the best model identified and is supported through the regression model summary seen in Exhibit 2.

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Exhibit 2.

The first step in model analysis is to confirm that each coefficients’ sign and size made sense within the demand equation. As mentioned, based on research and our hypothesis we expected real price to have a negative coefficient since basic economics tells us that as price declines, quantity demanded increases. We also reviewed the coefficients magnitude relative to quantity demanded to ensure their scale of impact made sense.

Following analysis of the size and scale of coefficients, each independent variables p-value was analyzed. Each variable has a p-value less than 0.01 which shows the statistical significance of each variable within the model. VIF values for each variable are below 3, ensuring that there is low multicollinearity between variables.

Moving to analyzing the model as a whole, the R2 value is 86.85%. This means that the model developed explains 86.85% of the variation in quantity demanded from 1990 - 2015. The S value is low relative to the size of the dependent variable of total beer sales which was always measured in the hundreds of thousands. The residual plots of the regression model present no irregularities and can be found in Appendix II. Taking all of these analytical perspectives together, we are confident that we have a statistically significant and accurate model.

While all statistical indicators in our model are strong, we want to point out that real world implications are important. An important distinction to define initially is the difference between causation and correlation. In the past several years, the rise of big data has led to the repetition of a key phrase, “Correlation does not imply causation” (Harvard Business Review, 2015). Our statistical analysis was conducted with real world data which is not a controlled environment, so we believe the results are a relatively reliable model which, when taken together, can explain 86.85% of the variance in demand. However, there is still 13.15% of variability unexplained, some of which is the systemic risk of doing business.

Lastly, looking at our demand variables and their representation of the macroeconomic climate and consumer behaviors, we can make some logical inferences about causality, as demonstrated by the sign preceding the coefficients. As expected in our hypothesis, price is negatively related to demand. As the industry produces greater efficiencies in their production processes and lower

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costs, firms can sell at a lower price. This is beneficial for Anheuser-Busch InBev because they already capitalize on brand acquisition through brewing at multiple locations to lower production and distribution costs.

In line with our hypothesis for beverage preference, an increase in overall per capita alcoholic beverage consumption will results in higher beer consumption. A downside to keep in mind, since overall beer consumption has dropped an increase in overall alcohol consumption, while beneficial to beer demand, will also increase consumption of wine and spirits (National Instituteon Alcohol Abuse and Alcoholism, 2016). Unemployment presents a surprisingly positive correlation to demand. As the national unemployment rate increases, so does the consumption of beer. Given additional time and resources, it would be interesting to explore whether wine and spirit consumption decline due to a perceived premium quality that is more attractive during economic boom and less attractive during economic downturn. The most surprising variable was the negative correlation between demand and the personal savings rate. As personal savings rate decreases, beer sales increase. This can be a function of an increase in either real disposable income without proportional increase in savings, or no change in real disposable income and a reduction in personal savings.

III. Forecasts

Since we have identified our key demand indicators and constructed their relationship through our demand equation, we are now able to forecast anticipated demand for 2016, 2017 and 2018.

The forecasts for the real price per barrel, personal savings rate, and percent change per capita in consumption for all alcoholic beverages were calculated using the average growth rate of the previous five years. We chose to use only the past five years in order to avoid inclusion in the forecast impacts of the Great Recession. This would have reduced the accuracy of the model because it would include trends that occur rarely. The calculated growth rate was then applied to the last given value from our data sets and extrapolated for 2016 – 2018.

Forecasted unemployment data was retrieved from the Congressional Budget Office to provide the most accurate data possible (US Congressional Budget Office). Our method of calculation used on the other independent variables resulted in an unemployment rate that fell below 4% which was not realistic given the previous 25 years of data. The forecasted values for the independent variables were then used in our regression equation to predict total sales for 2016-2018 as depicted in Exhibit 3 below.

Our projections predict a decline in beer sales that is most likely attributed to the continued decline in the unemployment rate and minimal change to the personal

savings rate. Total projected beer sales in barrels for 2016 – 2018 are: 209,774,000; 209,429,000; 209,960,000. The decline in 2016 and 2017 align with the continued decline of the unemployment rate, the slight rise in unemployment that CBO predicts in 2018 correlates to a slight rise in total beer sales.

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Exhibit 3.

IV. Alternative Scenario

One factor that we’ve identified as a good indicator of demand growth is the rate of personal savings per capita in the United States. Imagine that every American’s disposable income increased by 10%. Such a result would be a significant boon for the brewing industry: resulting in increased demand of 877,000 barrels and over $103 million in additional revenue (Exhibit 4). Inversely, significantly increased personal savings levels would prove to be catastrophic for the industry. Per industry recommendations, individuals should save between 10-20% of their disposable income (Pant, 2016). Even if personal savings rates were to top off at 10%, the disruption would be immense: demand would drop by nearly 7 million barrels, reducing industry revenue by more than $800 million (Exhibit 4).

Fortunately for the industry, there is a much greater chance of the first scenario occurring than the second. Currently, the per capita savings rate would need to nearly double for the second scenario to come to fruition. That said, we can see that the industry is sensitive to personal savings rates, and thus should keep attuned to overall savings trends. As Social Security and other entitlement benefits are eliminated in the future, it will be incumbent for individuals to save at increasing levels, thus reducing disposable income levels.

V. Application of Forecasts/Insights

Based on our forecast and demand sensitivity analysis discussed in the prior section we believe that the overall outlook for the industry will not significantly change.

Considering the current economic environment and customer preferences, our baseline forecast predicts a continued slow decline in overall domestic sales. This scenario does not change drastically when we looked at our best-case scenario of favorable economic circumstances which only shows limited improvements in sales overall (Exhibit 4). We see this as an indicator that even highly favorable economic circumstances will not reignite short term beer demand given the current shift in consumer preferences. In fact, in a potential worst case scenario sales could

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Exhibit 4.

decline sharply if economic variables such as savings rate increase drastically due to economic uncertainty.

Given the anticipated unchanged nature of the industry and the expected continued trend of declining demand, AB-InBev and SABMiller will not find significant growth opportunities in the current domestic market. This combined with the increasing competition by craft breweries who continue to enter the market, threatens the current market position and overall market share of both companies.

Because we predict that the domestic demand for beer will decline in the coming years, it is advisable for AB-InBev and SAB Miller to merge. The merger allows for greater penetration into desirable, untapped international markets for AB-InBev, as well as the opportunity for both companies to streamline their domestic supply chain and distribution networks. Process improvements and cost savings will be vital post-merger because the U.S. Department of Justice has required that the combined company divest it’s 58% stake of MillerCoors, meaning that there will be only marginal market share growth opportunities in the short term (Department ofJustice, 2016). Due to these restrictions, we do not expect there to be significant changes to the landscape of the domestic beer market as the Miller and Coors product portfolio will continue to hold a competitive position against the newly-formed company. Thus, the Stackelberg oligopoly structure will remain.

The analysis provided in this document is aimed to provide insight on the macroeconomic and consumer trend factors and their impact on the short-term domestic beer market. A newly-merged AB-InBev stands to gain significantly on the international scale, but current domestic trends indicate continued stagnation with limited growth opportunities, with the exception of strategic acquisitions. Our statistical model shows that domestic demand is highly dependent on the economic climate of the United States, therefore it is critical to monitor fiscal policy changes along with changing consumer preference trends. The company should focus on production, process, and distribution efficiencies to lower costs and boost operating margins. Because the domestic beer market is capped, it is vital that the company remains on the forefront of product innovation and diversity.

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Appendix I

Data Sources used for regression analysis

(Note: Grey highlighted series name indicate data used in final regression model)

Price

Series Name: Consumer Price Index - Average Price Data Wine (red and white table, all sizes, any origin)Source: Bureau of Labor Statistics, Consumer Price Index - Average Price Data, retrieved from bls.govLink: http://data.bls.gov/timeseries/APU0200720311?data_tool=XGtable

Employment Status

Series Name: Unemployment Rate - Seasonally Adjusted, 01/1948 - 10/2016Source: Bureau of Labor Statistics (2016-11-10). Employment and Unemployment - Seasonally Adjusted: Data-Planet™ Statistical Datasets by Conquest Systems, Inc. [Data-file]. Dataset-ID: 002-001-001 Link: http://data.bls.gov/timeseries/LNU04000000

Series Name: Misery Index, 01/1948 - 09/2016Source: Bureau of Labor Statistics (2016-01-18) retrieved from Data-Planet™ Statistical Datasets by Conquest Systems, Inc. [Data-file]. Dataset-ID: 002-036-001Link: http://dx.doi.org/10.6068/DP1588DC6298310

Personal Spending

Series Name: Personal Saving Rate (PSAVERT) Source: US. Bureau of Economic Analysis, Personal Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis Link: https://fred.stlouisfed.org/series/PSAVERT

Gender

Series Name: B01001 - SEX BY AGE Universe: Total population 2015 American Community Survey 1-Year EstimatesSource: U.S. Census Bureau, 2015 American Community Survey 1-Year Estimates, retrieved from http://factfinder.census.govLink: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_15_1YR_B01001&prodType=table

Age

Series Name: Annual Estimates of the Resident Population by Single Year of Age and Sex for the United States: April 1, 2010 to July 1, 2015Source: U.S. Census Bureau, Population Division, retrieved from http://factfinder.census.gov

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Link: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=PEP_2015_PEPSYASEXN&prodType=table

Life Stage

Series Name: DP02 - SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES 2010-2014 American Community Survey 5-Year EstimatesSource: U.S. Census Bureau, 2010-2014 American Community Survey 5-Year Estimates, retrieved from http://factfinder.census.govLink:http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_DP02&sr

Geographic Location

Series: Name: Per capita ethanol consumption for States, census regions, and the United StatesSource: National Institute on Alcohol Abuse and Alcoholism, Apparent Per Capita Alcohol Consumption: National, State, And Regional Trends, 1977–2012, retrieved from NIH, National Institute on Alcohol Abuse and Alcoholism, Link: http://pubs.niaaa.nih.gov/publications/surveillance98/tab3-6_12.htm

Beverage Preference

Series Name: Per capita ethanol consumption for States, census regions, and the United States (includes breakdown by beer, wine, spirit)Source: National Institute on Alcohol Abuse and Alcoholism, Apparent Per Capita Alcohol Consumption: National, State, And Regional Trends, 1977–2012, retrieved from NIH, National Institute on Alcohol Abuse and AlcoholismLink: http://pubs.niaaa.nih.gov/publications/surveillance98/tab3-6_12.htm

Series Name: Wine Consumption in the U.S.Source: Wine Institute, DOC, BW166/Gomberg, Fredrikson & Associates estimates. Preliminary. History revised.Link: https://www.wineinstitute.org/resources/statistics/article86

Appendix II - Regression Model Residual Plots

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VI. ReferencesAnheuser-Busch InBev. (n.d.). Anheuser-Busch InBev's (AB InBev) beer market share

worldwide in 2015, by country. In Statista - The Statistics Portal. Retrieved December 7, 2016, from https://www-statista-com.proxy.wm.edu/statistics/199024/ab-inbev-beer-market-share-by-country/.

Brager, D., & Greco, J. (2011, January 11). Nielsen Newswire. Retrieved from Nielsen.com: http://www.nielsen.com/us/en/insights/news/2011/millennials-redefine-the-alcohol-beverage-landscape.html

Census Bureau Reports. (2015, June 25). Unites States Census Bureau. Retrieved from https://www.census.gov/newsroom/press-releases/2015/cb15-113.html

Department of Justice. (2016, July 20). Justice Department Requires Anheuser-Busch InBev to Divest Stake in MillerCoors and Alter Beer Distributor Practices as Part of SABMiller Acquisition. Retrieved from https://www.justice.gov/opa/pr/justice-department-requires-anheuser-busch-inbev-divest-stake-millercoors-and-alter-beer

Fleissig, A. (2016). Changing Trends in U.S. Alcohol Demand. Atlantic Economic Journal, 263-276. doi:10.1007/s11293-016-9492-8.

Freedonia. (February 2016). Beer: United States. Freedonia Group.

Goldammer, T. (2008, October). The Brewer's Handbook. Retrieved from http://www.apex-books.com/: http://www.apex-books.com/

Harvard Business Review. (2015). BEWARE SPURIOUS CORRELATIONS. Harvard Business Review, 93(6), pp. 34-35.

Kell, J. (2015, March 23). 7 key milestones during the Miller Lite evolution. Retrieved from Fortune.: http://fortune.com/2015/03/23/miller-lite-beer-evolution/

Liberum. (October 2016). Beverages – Seven initiations The cash is still flowing . Liberum Capital Limited.

Mather, M., Pollard, K. M., & Jacobse, L. A. (2016). Population Bulletin: Aging in the United States. Retrieved from Population Reference Bureau: http://www.prb.org/pdf16/aging-us-population-bulletin.pdf

Mintel. (2015, November 27). Market Report: Beer US 2015. Retrieved from http://academic.mintel.com.

National Institute on Alcohol Abuse and Alcoholism. (2016, March). APPARENT PER CAPITA ALCOHOL CONSUMPTION: NATIONAL, STATE, AND REGIONAL TRENDS, 1977–2012. Retrieved from http://pubs.niaaa.nih.gov/publications/surveillance98/CONS12.htm: http://pubs.niaaa.nih.gov/publications/surveillance98/CONS12.htm

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Pant, P. (2016). How much of my income should I save every month? Retrieved from TIAA: https://www.tiaa.org/public/offer/insights/starting-out/how-much-of-my-income-should-i-save-every-month

Peltz, J. (1997, November 8). Sluggish Sales Are Heating Up Beer Price Wars. Retrieved from Los Angeles Time: http://articles.latimes.com/print/1997/nov/08/business/fi-51428

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