1. 1
Quality Over Quantity
The Income Effect and America’s Trend Toward Craft
Beer
Joey White
Furman University
3300 Poinsett Hwy
Greenville, SC 29603
April 2015

2. 2
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

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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.
II. Formulation of Model
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

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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 = α + xit’β1 + zi’β2 + μit
Where the dependent variable Yit is an indicator of growth in craft beer
production;
α is a constant term;
xit 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
‡ Bolded variables come from 2000 and 2010 centennialcensus,remainder from 2012 Brewer’s Almanac
§ This assumption can have some drawbacks that I will discuss in my conclusion.

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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
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

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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
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

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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
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
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

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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 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

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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
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.

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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,
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

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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.
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,

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Table 4: Regions
Dependent variable:% change in Breweries
VARIABLES Model 3 Model 4
incom(in %) 4.786***
-1.665
incom2 (in %) -0.0661***
-0.0238
winecon (in %) 0.485
-0.511
taxinc (in %) 0.293***
-0.105
retire (in %) 15.26*
-8.864
A21over (in %) -0.834
-1.254
avgagedf (in years) -14.76*
-7.761
license(1=yes) -27.92**
-13.01
coupons (1=yes) 4.59
-12.19
bgrocery (1=yes) -69.96***
-24.77
bconven (1=yes) 48.36**
-23.25
kegreg (1=yes) -0.0609
-10.52
distribrespclean
(1=distributor
responsibility) 6.249
-9.868
sunbeer (1=yes) -28.6
-19.68
neweng (1=yes) -29.07* -33.01**
-15 -14.44
south (1=yes) -25.48* -32.38**
-12.87 -12.27
west (1=yes) -23.44* -23.32*
-13.31 -13.33
Constant -11.87 60.70***
-72.24 -9.239
Observations 51 51
R-squared 0.524 0.15
Robust standard errors below coefficients
*** p<0.01, ** p<0.05, * p<0.1
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
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.

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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
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.

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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
Appendix 2
Appendix 1
Variable name Description Notes
neweng Dummy Variable for New England equals 1 if New England
midwest Dummy Variable for Mid West equals 1 if Mid West
south Dummy Variable for South equals 1 if South
west Dummy Variable for West equals 1 if West
License Control vs. License
equals 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 purchase
Bgrocery Beer Sold in Grocery Stores equals 1 if yes
Bconven Beer Sold in Gas or Convenience equals 1 if yes
Kegreg Keg Registration law
equals 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 cleaning
equals 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 yes
numbrew %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
avgagedf
Difference in median age from 2000-
2010
Appendix 1

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Appendix 3