Quality Over Quantity

The Income Effect and America’s Trend Toward CraftBeer

Joey White

Furman University

3300 Poinsett Hwy

Greenville, SC 29603

April 2015

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Quality Over Quantity

Joseph White

Furman University

December 2014

Abstract

In this paper I will give a brief introduction to the craft beer market and offer why it

is important to find what is driving the growth. I will offer my hypothesis that an

increase in average income fosters for an increase in number of breweries. My

quantitative empirical analysis will show that the relationship between growth in

income and growth in number of breweries has an inverse-U shape. Brewery

growth increases with income to a certain point, but at high levels of growth

brewery growth begins to decrease. Laws, regulation, growth in certain age sectors,

and wine consumption also influence brewery growth. I will conclude and present

intriguing data on regional craft brewery growth.

Joseph White

Furman University

Department of Economics

Empirical Methods of Economics

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I. Introduction

“I’ll have the imperial pilsner with a lime. I would like the lime on the side.” Last

summer I worked in a microbrewery and sold beer. Now, calling what I sold just

‘beer’, doesn’t give the microbrewery any credit. We had ten to twelve different

varieties of ales, stouts, lagers, and porters regularly on tap, with four small batch

beers that changed seasonally. The variety matched that of any regular alehouse,

with one exception – all the beer was made under the same roof. One day I would

come into work, and half the menu would change, soon to follow a flood of local

Chapel Hill residents anxious to taste the new small batch beers. There is no

question; American’s love their local breweries. From 2004 to 2011, the US has

increased from 1635 breweries to 2309*. Along with this growth has come the

overall growth of the craft beer segment. The craft beer segment includes specialty

beer produced by companies that make less than six million barrels of beer

annually. In the past year overall domestic beer production has increased by .5%

while craft beer production has increased by 17.6%†. The two major segments of

beer are imports and domestic. While the import segment has continued to grow at

6.9%, the domestic beer’s sales have not increased as fast. There is no question that

craft beer share is gaining in the domestic beer market. What has caused the gain is

the center of my discussion.

Predictably, craft beer should act like any normal good. As demand for more craft

beer grows, the supply of varieties of craft beer should also increase. But, what

* 2012 version of The Brewers Almanac † Brewers Association, http://www.brewersassociation.org/statistics/national-beer-sales-production-data/

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drives this demand and how could we predict future growth? As income increases,

the demand for more choices of beer should increase. Sort of like Maslow’s

hierarchy of needs, as people become more self-actualized, better educated and

presumably better off monetarily, they will tend to have a preference for variety.

The purpose of this paper is to determine whether changes in income drive the

growth in the craft beer market over time.

There is very little literature that looks at the effects of income on the growth in the

craft beer market in the United States. This is partially because the rise of the craft

beer market is relatively new. The microbrewery renaissance emerged shortly after

February 1977 when the government cut taxes for smaller brewers (Tremblay 118).

Under the new tax cuts, brewers producing less than two million barrels annually

only had to pay nine dollars a barrel. The cuts were so significant that the number of

specialty brewers grew exponentially. “In a subsample of firms, the excise tax

accounted for approximately 5 percent of the cost of goods sold for small specialty

brewers, but about 29 percent for mass-producing brewers in 2001” (Tremblay

119). These tax benefits gave brewpubs a competitive advantage to entering the

beer market and today have an important effect on growth.

Literature by Liesbeth Colen and Johan Swinnen looked at the effects of income and

beer consumption globally to find the relationship has an inverse U-shape. They

found that beer consumption initially increases with rising incomes, but at higher

levels of income the consumption falls. This pattern is an interesting finding because

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craft beer targets a more choice savvy audience; the audience likely increases in size

as income increases. Craft drinkers will emerge with an increase in wealth, but at a

certain point of income growth, craft growth might slow. Also, previous literature on

beer demand looks at the effect of advertising or effects on overall beer production

rather than the small craft beer segment.

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Literature by Toro-González, McCluskey, and Mittelhammer published in 2014

analyzes the demand for beer as a differentiated product and estimates own-price,

cross-price, and income elasticity for beer by type: craft beer, mass-produced, and

imported beer. This study analyzes data on more than 700 beer products

distributed in a 100-store chain with information on product and consumer

characteristics. It is the only academic literature that I found to distinguish craft

beer. The study confirms that beer is a normal good with inelastic demand, and

there are effectively separate markets for beer by type. The study suggests that

although these three types of beer fall under the category of beer, they are not close

substitutes for each other. Beer is a product where one develops a preference. A

domestic beer drinker might have trouble appreciating craft Indian pale ale (IPA),

and a craft drinker might simply refuse to drink domestic beer. Most notably, the

study found the average income level for a craft brew drinker to be $900 higher in

neighborhoods where more craft beer is sold compared to neighborhoods where

more mass-produced beer is sold (Toro-González 5). Another journal by Kenneth G.

Elzinga suggests that the entry of many craft brewers and increased product

heterogeneity has emerged consumption complementarities between wine and

beer. The journal claims that many of the new varieties of craft beer are possibly

getting the attention of wine drinkers. Wine might be a substitute for drinking craft

beer and vice versa.

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This paper looks at the effects of income

growth on the growth in craft beer market.

When determining how to set up my model, I

was restricted by the data available. To

measure growth in the craft brewery market

I use the percent change in number of

breweries by state from 2004 to 2011(my

dependent variable). For my independent

variable, I used the percent change in average

income by state from 2004 to 2011 to

measure income growth. I also controlled for

other factors that could have an impact on

the growth in the craft beer market. All of the

variables that I considered can be seen in

Table 1‡. The variable names used in my

regression can be seen in the left column

while the description is in the right column.

Like Colen and Swinnen’s model for analyzing

effects of income on beer consumption, I will adapt a similar empirical model

making use of a pooled OLS regression.

Yit = + xα it’β1 + zi’β2 + μit

Where the dependent variable Yit is an indicator of growth in craft beer production;

‡ Bolded variables come from 2000 and 2010 centennial census, remainder from 2012 Brewer’s Almanac

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is a constant term;αxit represents vector of time varying explanatory variables;zi represents a vector of explanatory variables that do not vary over time; &μit is the error term

I used % change in number of brewery’s in each state from 2004-2011 to be my

indicator of growth in craft beer production. My logic is that an increase in number

of breweries is closely correlated with an increase craft production. Almost all of the

new breweries that are built are on a small scale and would be classified as

brewpubs or microbreweries – regardless under the craft segment§. I used %

change in income in each state from 2004-2011 to be my first explanatory variable. I

also included this variable in squared terms to capture possible non-linear effects. In

addition, I used % change in excise tax rates per gallon of beer for each state from

2004-2011 to account for the possible effect of cost to produce in each state. The

greater the excise tax increase I would presume the less favorable to open a

brewery. Also, I used % change in population over the age of 21 from 2000-2010 for

my next explanatory variable. Although, the dates for this variable do not match up

with my dependent variable, this will account for the expected lag effect that occurs

when breweries are determining to open. A brewery that opens in 2012 will look at

the growth in population over 21 from 2011 or before when making decisions. Next,

I used % change in retired population for each state and % change in wine

consumption for each state. My presumption is that those that are retiring are more

likely to take the time to appreciate the varieties of craft beer or wine. I include %

change in wine consumption to account for any substitution effect. Finally, I used

multiple binary variables to account for laws and regulations for each state. The

§ This assumption can have some drawbacks that I will discuss in my conclusion.

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license variable controls for a state’s regulation on beer sales – license states simply

tax on beer production, while control state’s production is controlled or mandated

by the state. The variables Bgrocery, Bconven, and Bsunday show whether states

allow beer sales in groceries, convenience stores, and on Sunday. Major cities within

each state can append the state law, but these variables again reflect the laws of

each state. I predict that looser laws on sales would be more favorable for growth in

number of breweries. Kegreg is another binary variable that controls for whether a

state requires keg registration. Keg registration is costly for businesses to perform,

and states with keg registration may be at a disadvantage to states that do not

require the law.

III. Data

The data on % change in number of breweries was constructed in the following way.

From the 2012 edition of The Brewers Almanac, I collected data on the number of

breweries for each state for 2004 and 2011; I created the percentage change by

subtracting the 2011 value by the 2004 value then dividing this value by the 2004

value. Once I did this, I multiplied the result by 100 to get the % change in number of

breweries from 2004-2011. The data on all the % change variables were

constructed in a similar fashion. I received the data for all the non-bolded variables

(seen in Table 1) from the 2012 edition of The Brewers Almanac; the bolded

variables come from the 2000 and 2010 Centennial Censuses. Data on the difference

in median age from 2000 to 2010 was calculated by subtracting the 2010 median

age by the 2000 median age for each state. The variable is a number variable

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interpreted as: a one-year increase in the difference in age may yield a certain

percentage change in number of breweries. I did not include the variable on %

change in beer consumption because the data available only included overall beer

consumption (craft, import, and domestic), which would bias my results. My

argument for including wine consumption is that wine could be a close substitute

for craft beer. These represent the vector of time varying explanatory variables in

my model.

The remaining binomial variables (listed in Table 1 from license to Sunday sales of

liquor) represent laws and regulation for each state and are given a value of 1 if yes

to variable and a 0 if no to the variable. See Appendix 1 for the extensive list of

variables meanings and number assignments used in my regression. Also, the

regions align with the specified regions established by the US Census. These

represent a vector of explanatory variables that do not vary over time in my model.

Table 2 shows the descriptive statistics of the vector of time varying explanatory

variables. In the first row, we see that for the average state, the number of breweries

increases by 38.59% and the highest growing state increased by 147.619% while

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the lowest growing state decreased by 25%. In the fourth row, we see that for the

average state, the median income increased by 24.74% and the highest growing

state’s income increased by 57.85%, while the lowest only increased by 5.55%. In

the last row, the median age difference for the average state increased by two years.

On average, the United States saw the median age increase by two years from 2000

to 2010.

IV. Empirical Results

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The results for the pooled OLS

regressions are presented in Table 3. The

variables I used for the regression are

seen in the first column while the models

are observed in the second and third

columns. The first model was my original

model that I regressed prior to checking

for heteroskedasticity. All varying

explanatory variables in the first model

appeared to be normally distributed, with

the exception of income, which appeared

to have an inverted-U shaped relation to

% change in number of breweries. I

tested for homoscedasticity using the

Breusch–Pagan test for

homoskedastisicity on the first model to

find that I could reject constant variance

at 89% significance. Although, this wasn’t

quite a 90% significance I decided to go

ahead and assume heteroskedasticity.

Model 2 is the same as Model 1, but

includes a robustness check. The results of assuming heteroskedasticity are

significant to certain variables in the model. Income is now statistically significant at

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a 95% level, along with % change in taxes on beer and % change in retirement

population at a 90% level. The binominal variables including average age difference,

license requirement, beer sold in groceries, and beer sold in convenience stores are

all significant at a 99% level.

After developing the model for robustness, I tested for omitted variables in the

model to find that the model has no omitted variables. Next, I checked for

multicolinearity in the regression to find that only incom and incom2 are highly

correlated with one another. This result was expected because incom2 is a quadratic

term for incom. No other variables seem to be highly correlated with each other.

Then I checked for outliers using Avplot function to find potential outliers based on

each variable. Refer to Appendix 2 for my Avplot results. Based on these findings, I

found that there are not any states that are consistently outliers across multiple

variables, the outliers that can be observed are covered by the assumption of

heteroskedasticity. After, I checked the normality of the residuals of my regression

using the Kernal density estimate, see Appendix 3. After performing the Swilk test

for normality, I found that the residuals are in fact normally distributed. Finally,

having checked for normality, outliers, multicolinearity, omitted variables, and

homoskedasticity I checked to see if all my variables are jointly different than zero. I

ran an f-test and found that the variables were.

V. Interpreting Results

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The robustness model does not change any of the coefficients but does make certain

variables that I originally predicted to be more statistically significant. The variable

for income now is statically and economically significant. The predicted value states

that holding other variables constant, for every % increase in income will result in a

3.852% increase in number of breweries. At a certain point in growth, the number

of breweries will start to decrease - a result of the quadratic term, incom2. The

maximum income that will output the greatest growth in the number of breweries

can be calculated by taking the partial derivative of the dependent variable in

accordance to income.

numbrew/incom= 3.852 - .115incom

0 =3.852 - .115incom

income=33.496

At a percent growth of 33.496, the % increase in number of breweries is estimated

to be 127.094%. In Table 2, the maximum % increase in number of breweries was

147.619%, which occurred in Indiana. Indiana fits this model relatively well

considering its % increase in income was 30.265%. Wine consumption did not

seem to be statistically significant, but I am controlling for the substitution effect, so

I believe that the inclusion of the variable is still important for the unbiasedness of

the regression. The % increase in taxes on beer is statistically significant, but

economically confusing at first glance. The coefficient taxinc is positive, which may

go against most economic principles – an increase in taxes yielding a growth in

number of breweries. However, a tax increase could be a government reaction to a

well performing beer market. Since my model does not show a lag effect for taxinc,

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the variable can come across as economically insignificant. The increase in %

change of retirement population is both statistically and economically significant. A

% increase in retirement population yields a 13.67% increase number of breweries.

This increase matches with my prediction that older populations would have a

preference for exploring craft beer selections. The percent change in people older

than 21 is neither statistically or economically significant. On the other hand, the

average difference in years is both statistically and economically significant. The

variable says that a 1-year increase in median age will result in a 20% decrease in

number of breweries. This goes against intuition about an increased older

population results in brewery growth; this may suggest that there are specific age

populations along with retirees that prefer craft beer.

The explanatory variables that do not change over time are all mostly very

statistically significant and only economically significant in certain instances.

License states on average grew 39.05% less than states that were controlled. The

reason could be that control states can make more favorable circumstances for

breweries to prosper. A control state may pick and choose regulation on beer

varieties. This power allows a control state to have a much greater impact on the

supply side of beer. License states, simply tax beer to control supplies. Variables that

pertain to regulation of where and when beer can be sold, i.e. sunbeer, seem also to

have negative impacts on growth in number of breweries. This could be caused by a

trend that states with looser regulation on beer sales, may also tax more.

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Table 4: RegionsDependent variable: % change in BreweriesVARIABLES Model 3 Model 4incom (in %) 4.786***

-1.665incom2 (in %) -0.0661***

-0.0238winecon (in %) 0.485

-0.511taxinc (in %) 0.293***

-0.105retire (in %) 15.26*

-8.864A21over (in %) -0.834

-1.254avgagedf (in years) -14.76*

-7.761license (1=yes) -27.92**

-13.01coupons (1=yes) 4.59

-12.19bgrocery (1=yes) -69.96***

-24.77bconven (1=yes) 48.36**

-23.25kegreg (1=yes) -0.0609

-10.52

distribrespclean (1=distributor responsibility) 6.249

-9.868sunbeer (1=yes) -28.6

-19.68neweng (1=yes) -29.07* -33.01**

-15 -14.44south (1=yes) -25.48* -32.38**

-12.87 -12.27west (1=yes) -23.44* -23.32*

-13.31 -13.33Constant -11.87 60.70***

-72.24 -9.239Observations 51 51R-squared 0.524 0.15Robust standard errors below coefficients*** p<0.01, ** p<0.05, * p<0.1

VI. Conclusions

No model offers the perfect answer. The

model I have developed offers relatively

accurate predictions of % growth in

breweries if the given the information on %

change variables and other explanatory

variables that do not change. I have

developed an income growth that will also

maximize the growth of breweries. If a state

grows at 33%, we could expect the

maximum growth for breweries. So my

model does support the work by Colen and

Swinnen, that an income follows an

inverse-U relation to craft beer growth.

However, the model does also have many

drawbacks. First, the dependent variable, %

change in number of breweries,

disproportionally measures craft beer

growth in the brewpub sector. Many craft

beer companies experience growth in

production and sales without opening more

breweries. In my model, Florida has a

negative growth in number of breweries

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from 2004 to 2011, but during this time Florida had grown to be one of the largest

craft beer exporters (Watson). Next, my binary variables don’t paint the best picture

of each state’s regulations. Many states have regulations that are nullified by local or

city law. City level data might have been a better way to pinpoint the relative effects

of laws. With so many laws and ways to regulate beer sales and production, it might

have been more beneficial for me to analyze the craft beer market on a state level

rather than a nationwide one. My best regression on craft beer growth will need

more accurate measures of growth and analyzed at a micro level.

VII. Further Discussion

Using my same model, I wanted to see if there were any trends in brewery growth

and geographic regions of the United States. Table 4 illustrates two regressions I ran

to detect any regional growth. The variables neweng, west, and south are dummy

variables to represent respective states in each region. The variable midwest is not

included in the regression because it is the variable that neweng, west, and south

are compared. Model 3 is my original model these variables added, and Model 4 is a

simple regression of the region variables on % change in number of breweries. The

difference between the models is very small when interpreting each region variable;

however I will interpret Model 4 because it is the most straightforward model.

The variables neweng, south, and west all have negative coefficients meaning that

each of these regions has had less brewery growth from 2004 to 2011 than the

Midwest. The neweng variable can be interpreted; New England’s number of

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breweries is growing at a rate 33.01% less than the Midwest’s. The variables south

and west also are interpreted in a similar fashion. It is apparent that the Midwest

region, which is composed of Indiana, Wisconsin, Michigan, Nebraska, Illinois, Iowa,

Kansas, Missouri, North Dakota, South Dakota, and Ohio, have favorable market

conditions that has induced a large growth in the number of breweries. The

Midwest’s vector of time varying explanatory variables are all very similar in values

to the other regions, and this leads me to believe that the region likely has a

competitive advantage.

Looking at the supply side of production, beers composed of three main ingredients

– hops, barley, and water. 98% of hops are produced in the West**. Barely is grown

throughout the US, with majority of production in the Western states††. But, water is

very plentiful in the Midwest states. Indiana, Wisconsin, Michigan, and Ohio all

reside on the great lakes while the other states have plentiful amounts of freshwater

lake and river reserves. I didn’t originally include water accessibility in my

regression, but it is possible that the Midwest states have lower costs of production

due to ease of accessibility to water.

** http://www.usahops.org/index.cfm?fuseaction=hop_info †† http://www.barleyfoods.org/facts.html

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References

Colen, Liesbeth, and Johan F. M. Swinnen. "The Determinants of Global Beer

Consumption." Beer Drinking Nations 270 (2010): n. pag. SSRN. Web. 3 Mar.

2015. <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1752829>.

Elzinga, Kenneth G. "The U.S. Beer Industry: Concentration, Fragmentation, and a

Nexus with Wine." Journal of Wine Economics 6.02 (2011): 217-30. Web.

Toro-González, Daniel, Jill J. McCluskey, and Ron C. Mittelhammer. "Beer Snobs Do

Exist: Estimation of Beer Demand by Type." Journal of Agricultural and

Resource Economics 39.2 (2014): 1-14. Web.

Tremblay, Victor J., and Carol Horton. Tremblay. "Imports and Domestic Specialty

Brewers." The U.S. Brewing Industry: Data and Economic Analysis. Cambridge,

MA: MIT, 2005. 103-34. Print.

Watson, Bart. "Where the Craft Breweries Are." Brewers Association. Brewers

Association, 4 Sept. 2014. Web. 2 Mar. 2015.

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Appendix

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Appendix 2

Appendix 3

Appendix 1Variable name Description Notesneweng Dummy Variable for New England equals 1 if New Englandmidwest Dummy Variable for Mid West equals 1 if Mid Westsouth Dummy Variable for South equals 1 if Southwest Dummy Variable for West equals 1 if West

License Control vs. Licenseequals 1 if license state equals 0 if control state. License state simply taxes beer sales, while control states control distribution or sale of alcohol.

Coupons Coupons equal 1 if yes, coupons are allowed to discount alcohol purchaseBgrocery Beer Sold in Grocery Stores equals 1 if yesBconven Beer Sold in Gas or Convenience equals 1 if yes

Kegreg Keg Registration lawequals 1 if yes, if required It is a hassle for the average keg seller to have to write down the address and ID number from everyone who buys a keg from them, and the additional tax is a burden they do not relish

Distribrespclean Reponsibility for draft line cleaningequals 1 if distributor's responsibility, equals 0 if the retailers responsibilty. Distributor responsibility puts cost on craft beers to ensure lines are cleaned in contracted sites

sunbeer Sunday Sales of Beer equals 1 if yesnumbrew %change in # breweries,2004-2011

winecon% change in Per capital consumption of wine (gallons) 2004-2011

taxinc% change in Excise tax rates per gallon of beer 2004-2011

incom% change in Avg per capita income 2004- 2011

gdp % change in GDP from 2004-2011

disincom% change in personal disposable income from 2004- 2011

retire% change in per capita retirement 2004-2011

21over% change in pop older than 21, from 2000-2010

avgagedfDifference in median age from 2000-2010

Appendix 1