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What Factors are important determinants of demand for beer in
the USA?
Hypothesis: I will test to see if a high temperature increases the
consumption of beer.
People around the country wonder when they are sitting at a bar in which state do
people consume more beer. I myself have wondered the same thing, and then started
to wonder what different variables affect the consumption of beer. Is it the weather? Is
it the unemployment rate? Or is it something else?
Did you know that in some states about 35% of the price of the beer that you are
consuming is part of tax?
I am going to create a model that will tell us how the demand for beer is affected, by
different variables.
For this I am going to gather data from each state in 2010, and then compare it and see
if there is any significance in each one of the variables.
My biggest question always was if the wormer it is in a state the more people tend to
consume beer, and I cannot wait until I see if there is any significance at all.
Besides testing my hypothesis I will demonstrate how the demand for beer behaves
within the different variables, and I will then test to see how temperature affects the
consumption of beer compared to other variables like income per capita, and others.
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This should be an interesting challenge to see if I can get the most important variables
that ultimately have significance on the consumption of beer.
Literature
The Demand for Beer
Thomas F. Hogarty and Kenneth G. Elzinga, May, 1972
This article talks about what different factors affect the demand for beet, such as price
per case of beer per state, and income per capita, and others.
These different variables all affect the demand for beer, and this project does a good job
when it comes to pointing out the different variables that could change the demand for
beer.
It is very important that I look at this project carefully, since it could be very helpful on
the development of my own, since it talks about everything that I had in mind except for
the weather variable that I would like to add and see if it improves the model.
The project also talks about the elasticity of demand for beer, showing us how elastic
this product is to the different changes that could occur, and according to Thomas F.
Hogarty and Kenneth G. Elzinga (1972), beer is quite elastic.
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Beer in Good Times and Bad: A U.S. State-Level Analysis of Economic Conditions and
Alcohol Consumption May 2009
Donald G. Freeman, Ph.D.
This article refers to the beer consumption and the economic conditions at a particular
time. It compares the demand for beer when the economy is bad and when the
economy is good. It also compares the economy to the consumption of other alcohol
beverages, but for our concern we will be focused only on the beer matter, and not on
the other beverages.
This is a very good article to base my project on since I will be looking for different
variables to test with, including economic condition of a particular state.
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The Model
Coi = B1 - B2Txi + B3Inci + B4Tmpi + B5 Agei + B6Gdpi - B7Uni + B8Dtxi + e
This is the model that I will be testing, using all of these variables to look at the demand
for beer, and to test my hypothesis.
First I started by using the variable Txi which are the state taxes on beer in the ith state in
2010, and this particular variable will tell us if the increases in taxes on beer will have
any effect on beer consumption, which I personally think that they will. This variable has
a negative sign since in my opinion the higher the taxes are, the less people will
consume beer in that state.
Another variable that I thought it would be important to have was Inci, which is Per
Capita Income in ith state in 2010, which will tell us if the income that people make in
that particular state has any effect on their consumption of beer as well, which could be
very important since people with less income might not spend as much money in beer.
This variable has a positive sign since I believe that the more income a person makes,
the higher the probability is that that person will spend more money of beer.
Then I looked at the variable that I have wanted to look since I started the project,
which is Tmpi, which is the average temperature in ith state in 2010, which could also
have a major impact on the consumption of beer, and this variable has a positive sign
since I believe that the higher the temperature is in that state, the more consumption of
beer there will be, because it will make people wanting to drink more.
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I have also introduced a variable called Agei, which is the number of people over 21
years of age in ith state in 2010, which will also have a positive impact on the
consumption of beer, since the more people that the state has that are 21 years of age
or older, the more chance there will be that that state consumes more beer.
Gdpi is another variable that I will use, and this one in the Gross Domestic Product of
2010, and it will have a positive impact since the higher the GDP is in that state, the
more people will consume in my opinion.
Finally, I also used Uni, which is Unemployment Rate in ith state in 2010, and this one will
have a negative impact, since the higher the unemployment rate is in that particular
state, the less people will consume beer.
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Model Estimation, Hypothesis Testing, and Interpretation of the Results
To estimate the appropriate model and to test my hypothesis I will be using a program
called SAS. For my hypothesis test I will be using a 5% confidence interval.
After first running a regression on the entire model with all the expected variables the
results were pretty interesting.
The first model I run was the following:
Model 1
Co = Tx Inc Tmp Age Gdp Un
The results can be found on the attached table (1). These results were not very
significant for the majority of the variables, since only Un made it in the 5% confidence
interval, with a t-value of -2.29, which is pretty significant.
By running this model my hypothesis would have to be rejected, since in this case
temperature is not significant enough, and even though it is not significant it also has a
negative signal, meaning that the lower the average temperature is in that particular
state, more consumption of beer there is.
All the other variable signs were just as I expected, except for income where the signal is
negative showing us that the more income a person has, the less beer they consume,
and for temperature, where the signal was negative.
In order to try to fix the model I will attempt to remove some of the variables that I am
staring to think that have no significance for the demand of beer, like the number of
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people that are 21 in a particular state (Age), and the taxes on a 12 OZ bottle of beer in
a particular state (Tx). I am also going to change the way temperature if run in the
model, trying to square it since it does not seem like temperature would be a linear
function, making this part of the reason of the insignificance of this variable (Tmp).
Model 2
Co = Inc Gdp Un Tmp Tmp2
After running this model the output is much better. This makes this a very good model
to begin with. These results can also be found on the attached table (2).
Income per capita shows really good significance having a negative sign still, meaning
that the less average income a person gets in a particular state, the less consumption of
beer there is.
Gdp per capita still does not have any significance still, making me think that this
variable is not going to be significant for this model. The signal still is positive, meaning
that if Gdp were to be significant, it would make consumption increase with an increase
in Gdp as well.
The Unemployment Rate still has a very good significance, and maintains a negative
signal, showing us that the more unemployment there is, the less consumption of beer
will be. This makes my expectations look good, since it was what I expected.
The last variable that I ran was Tmp2, which is the squared temperature, making it a
non-linear function. This seemed to make more sense and it reflected on the model as
well, meeting the expectations. Even though there is not enough significance to accept
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this variable, it seems that we are on the right way there, improving much more. The
signal changed when compared to Tmp becoming positive, meaning that if the average
temperature in a particular state is high, the consumption of beer will also be higher in
that state.
The intercept is also very good for this model, having a positive sign and a good t-value.
The only bad statistics that I seem to find in this model would be the adjusted r-square,
which is pretty low (0.144), even though it has increased from the last model (0.142).
I am now going to ad tmp3, which is the cubic function of tmp (temperature) to see if it
improves the variable even more. I am also going to add another variable called Dtx,
which is the deflated taxes in 12 Oz bottled beers in a particular state, since some states
have not changed their taxes on beer for about 20 years, which is making my Tx variable
not matter to this model. This way I would find a price variable for the model, making it
a much better one. I will also jump and make this variable a cubic function, since it
seems to me that it would be the same way as the tmp variable.
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Model 3
Co = Inc Gdp Un Tmp Tmp2 Tmp3 Dtx Dtx2 Dtx3
After looking at the results I found a major improvement in the overall model. All of
these variables are really significant and have great t-values. The income sign is still
negative, meaning that the less average income a person gets in a particular state, the
less consumption of beer there is. This came to me as a surprise since I was expecting
that income would have a positive effect to the consumption of beer.
The Gdp sign is still is positive, meaning that consumption increases with an increase in
Gdp as well, as expected.
The Unemployment Rate still has a very good significance, and maintains a negative
signal, showing us that the more unemployment there is, the less consumption of beer
will be. This result was also as expected since people without a job have less money to
spend in alcohol. On the other hand, some could say that people without a job have a
reason to consume more beer.
Temperature (Tmp3) shows very good results and its sign is positive. This is do to a
change in the interpretation of temperature data as a linear function. Instead I chose to
interpret this variable as a cubic function.
This model shows that my hypothesis is correct, so in this case I would have to accept
the null hypothesis, proving that temperature does have a positive effect on beer
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consumption. The higher the average temperature is in a particular state, the more
people will consume beer in that particular state.
Finally, has expected the Deflated Taxes variable (Dtx3) has a very good significance and
it also has a positive sign, showing that the higher the deflected taxes are in that state,
the higher the consumption of beer in that state is.
My final model for the demand of beer would follow as:
Co = -268.64 – 0.0006 Inc + 0.0002 Gdp – 1.17 Un + 20.08 Tmp – 0.41 Tmp2 + 0.003
Tmp3 + 733.73 Dtx – 34373.0 Dtx2 + 301553 Dtx3 + e
Even though this is a good model, I would still like to test for heteroskedasticity, since
these variables look to good to be true.
Therefore, after this test, the standard errors changed a little bit, making the t-values on
all the variables change as well. Overall the model is still pretty good, with a really good
significance.
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Conclusions
After finding the model that best suits my expectations, I would have to say that this is a
pretty good model, even though there are some limitations about it. Some of these
limitations were for example, the lack of pricing variables, since I could only find
significance in the deflated taxes per state in 2010. No other pricing variables except for
this one were relevant.
Although there is only this variable to define the significance in change of pricing, it still
suits the model really well, since the overall significance seems to be pretty good.
The model tells us that temperature does have a positive effect in the consumption of
beer, which was what I was testing for to begin with.
There were some surprises in this model such as the fact that the variables Age and Tx,
which are respectively, people over 21 years old in ith state and taxes on 12 Oz bottles
of beer in ith state, were not significant. These variables were expected to be somewhat
significant since people under 21 years old cannot drink legally, and since the higher the
price of beer (in this case 12 Oz bottles) the less people would probably tend to drink (in
this case price would be affected by taxes).
Finally, this model also tells us that income has a negative effect on the consumption of
beer, which was also not expected, but overall the model is significant and teaches us
not to always believe in the expectations.
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References
Beer in Good Times and Bad: A U.S. State-Level Analysis of Economic Conditions and
Alcohol Consumption May 2009
Donald G. Freeman, Ph.D.
http://www.beeronomics.org/papers/1A%20Freeman.pdf
The Demand for Beer
Thomas F. Hogarty and Kenneth G. Elzinga, May, 1972
http://www.jstor.org/stable/1926282
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Dependent Variable: Consumption of Beer per Adult in ith State in 2010
Regressor
(1)
(2)
(3)
Heteroskedasticity
Inc
-0.0003
(0.0002)
0.0001
(0.00008)
-0.979
(0.427)
-1.24
(1.564)
-2.149
-0.00037
(0.00019)
0.00009
(0.00008)
-1.046
(0.399)
-
-0.00056
(0.00018)
0.00017
(0.00007)
-1.168
(0.389)
-
-0.00056
(0.000013)
0.00017
(0.000037)
-1.168
(0.423)
-
-
-
-
Gdp
Un
Age
Tx
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Dtx
(2.796)
-
-
Dtx2
-
-
Dtx3
-
-
Tmp
Tmp2
-0.029
(0.093)
-
Tmp3
-
-0.296
(0.64)
0.0022
(0.006)
-
Intercept
50.334
(7.969)
58.032
(17.037)
Summary Statistics
SER
Adjusted R2
n
4.76965
0.1424
51
4.76609
0.1437
51
733.7
(290.8)
-34373.0
(12865.0)
301553.0
(119870.0)
20.08
(8.58)
-0.409
(0.162)
0.0027
(0.001)
-268.64
(148.02)
733.7
(302.137)
-34373.0
(14393.0)
-301553.0
(133801.0)
20.08
(8.184)
-0.409
(0.1577)
0.0027
(0.00099)
-268.64
(141.817)
4.28426
0.3080
51
4.28426
0.3080
51
* The coefficient is statistically significant at the 5% level.
* Standard Error in Parentheses
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