This document provides an executive summary and background for a study analyzing total meat expenditures in US households. Key findings include: - Combined meat expenditures increase with income, though insignificantly, while expenditures on specific meats like beef and pork do not. - People have preferences for certain meats, indicating some substitution between meat types. African Americans spent the most on total meat. - Meat consumption decreases with education and is lower in rural vs. urban areas. The study uses data from the 2014 Consumer Expenditure Survey to analyze factors like age, race, region, income, and education that influence meat purchasing.

1. The A Team
Anuva Bhuyan, Eric Bosse, Aigul Mukanova
12/3/2015
Professor Nicolas Williams
“Total Meat Expenditures and where to allocate
meat advertising and marketing”

2. Executive Summary
 Combined total meat expenditures are a normal good, meaning as
income increases, household expenditures on total meat increases
(although it is statistically insignificant (p value =0.6984
after corrected for heteroscedasticity)).
 However, Beef, Pork and Other types of meats are not normal
goods, while seafood and poultry are (p values also insignificant
for log of income for all meat variables).
 As increases in independent meat variables, there are decreases
in the dependent variable, due to the negative coefficients on
the dummies that don’t represent that variable. Meaning that
generally, people have a preference on which type of meat they
choose. This also indicates that to an extent, meat against other
types of meat are somewhat substitutes.
 When looking at each race in the model, mixed people tended to
have the highest meat consumption in all categories except
poultry, where Hawaiians had the highest coefficient.
 However, since Asian, Native American, Hawaiian and mixed people
make up less than 7% of the total sample. With that being said,
they were made into a nonwhite-nonblack communicative variable.
When looking at that data set, African Americans on average spent
the most on total meat.
 It was also noticed that people in rural areas ate less than
urban areas. This was another variable considered insignificant.
 Another thing we noticed that no matter what happens to income,
or other factors, there are some people than just do not eat
meat, whether it comes from religion, culture, taste, preference,
etc.
 Another point is the more educated someone is, the less likely
they are to eat meat.
 When looking into a model that incorporated zeros to see how
vegetarians affected the model, we see that meat expenditures for
older people increase are almost one and a half times more than
younger people (no variables statistically significant)
 However, when looking at a model only those who consume meat, we
see that meat expenditures decrease with age (no age dummies
statistically significant)
 By looking at the dependent variables as logs, we can see
elasticities of different meats

3. Body
The topic that the A Team choose to do was to look at total meat
expenditures on for a household from the Consumer Expenditure Survey,
and our hypothesis was to test if meat was a normal good. The
background behind it comes from interaction with a wide variety of
students in the Masters in Applied Economics programs. There’s a wide
variety of students from different parts of the world, different
religions and cultures, different tastes and preferences. We wanted to
see how meat was purchased across the United States and to see if
there were any drastic changes in demographics.
We believe that we will learn how different people in different
regions consume meat. We find it interesting because we want to think
about what factors change these types of patterns. Why would someone
living in a rural area not eat a certain type of meat? Would seafood
expenditures be higher in the northeast because of such an industry of
fishing? Would certain races be prone to higher expenditures of meat?
This is what we hope to discover.
The data we pulled from came from the Consumer Expenditure
Survey, and we used data from the first quarter of 2014. The data
pulls from the observations in the form 2 consecutive one week periods
for each respondent. We used different types of dependent variables to
discover how total meat as well as individual meat expenditures
change. Moreover, there was essentially 3 types of dependent variables
used, meat variable that included zeros, meat variable without zeros,
and the log of the meat variable. The variable of meat with zeros was
heavily skewed to the right, while the variable of meat with zeros was
also skewed to the right and the log of meat was a normal
distribution.
The most important independent variables were the age of the head
of household, race, region, rural area, education, income or more
accurately the log of income, and family size. These are consistent
with a similar model run by the British Food Journal in 1993 using
data from the UK Family Expenditure survey. Their findings are
consistent with the results done in our models. They also make an
important assumption that they incorporated into their results. Both
the Consumer Expenditure survey and the UK Family expenditure survey
gather data within a two week period during a specific period. Since
spending patterns do not often change from week to week, the British
Food Journal makes the assumption that if someone was going to
purchase any sort of meat, they would have done so during the period
in which they were being surveyed. This aligns with the assumption
that we made earlier, in that there are some people who do not
purchases any sort of meat for any various reasons.
This could come from reasons proposed earlier, such as race, age
region, etc. We believe that health risks and vegetarian preference
have a considerable effect as to why there is such a difference. Using

4. a frequency chart we discovered that roughly 38% of the respondents of
the survey did not purchase any sort of meat during that two week
period. It’s safe to say a vast majority of those do not consume meat
at all.
Below is a frequency chart of all those who have purchased meat
in the sample (0=no purchase, 1 equals purchase).
Our regressors come from intuition and economic logic. First off,
Age of the head of household is important because it distinguishes how
old the person is, which is a direct reflect of how someone is likely
to care for their body. Older people are more likely to be more
consciously aware of their health, and therefore they might not eat as
much meat to avoid some health risks, however we will find otherwise.
Race of the person is important as well because different people have
tastes, as well as certain races possibly being associated with a
certain religion. Not all people are build the same, and therefore
there might be some changes to be seen there.
As proposed before, region is an important because of the
breakdown of where these types of meat are produced. For example,
there might be more cow farms for grazing in the Midwest than
elsewhere, making transportation costs lower and therefore a lower
price for beef in the Midwest, or seafood in the Northeast because of
easy transportation from the shores to the stores. To go along with
that, being in a rural area might also give some sort of relevant
change in meat purchases. Rural habitants might produce their own meat
products by raising cattle and poultry or have easier access to those
goods through connections to fellow farmers.
Education was we believed to be important because we figured that
the more educated someone is, the more they understand or have access
to nutrition information or long term effects of certain foods on the
human body, deterring them from purchases. Income, or as we used the
log of income to normalize it, is often shown to have a positive
relationship with education, might allow higher levels of income,
which means they can afford to purchase more meat. Family size was
another variable used because we believe the larger a household is, it

5. might be more expensive for someone to purchase meat for a meal,
compared to cheaper non-meat alternatives.
As brought up earlier, one transformation we performed was to use
the log of income (lnfin) rather than actual income, because this will
make it more normal and allow more accurate data. We created dummies
(beefb, poultryb,…etc.) that indicated if an observation purchased
that type of meat (0=did not purchase, 1=there was a purchase). The
education dummy was created and broken up into different categories.
We used a base of did not graduate high school, even though the data
broke it up into more categories, we decided to make them all one
base. This lowered our outliers and multicollinearity. The data was
created where the highest level of education was used, meaning a
Bachelor’s degree was a 15, and a Master’s degree was a 16, and there
was no repeats (meaning for example if someone had a Master’s, they
did not have 16 and every number below). When we created those dummies
of education, we followed a similar principal, where there is no
repeated values. For example, if an observation graduated college,
they had 1 in Bachelor’s dummy and a 0 in every other education dummy.
We also build race dummies as well. These dummies were created
from the ref_race variable, where the head of household answered what
race they identified as. From there we broke them up into individual
dummies in an attempt to see if the change in race was nonlinear. We
created wht, black, mixed, NatAmer, Asian, and Hawaiian. But since
combined, mixed, NatAmer, Asian, and Hawaiian only made up of 7% of
the data set, we ran models where there was all of them separate and
combined into oth. Those ladder models had wht black and oth to
describe the race of the observations. Both models used wht as the
base because they make up most of the data set and US population.
Region models were also made to break up the four regions, NE, MW,
South, and West, to see how changes in meat purchase occurred across
space. NE was used as the base because they are the most concentrated
in terms of space in America, while South had the most observations in
the data set. Interaction dummies were also created to see which race
in which region had the highest expenditure on a certain type of meat.
Those interactions were built by multiplying a race by a region, for
example whtmw indicated a white person in the Midwest.
Age dummies were also created to investigate a nonlinear
relationship with people at different points in their lives. Given the
(theoretically) continuous variable age_ref, we broke it down into 10
year brackets designating which decade that person is in when they
took the survey. For example, if an observation answered 35, then they
would receive a 1 in the dummy thirties and zeros in every other
category. The dummies twenties (which was the base) and eighties are
slightly different than the rest. In the case of the twenties, we
included those under 20, because those households who were 18 and 19
were so few, it was similar to include them in the twenties. The other
scenario was due to top-coding everyone older than 83 into one number.
Since the survey had everyone over the age of 83 to be clumped into
one age of 87, because those observations are distinguish able,

6. someone might be able to pick out who those people are outside of the
survey. The CES did that to conceal their privacy. We also looked into
the marriage status the head of household, to see if changing of
marital status affected shopping patterns and possibly if people
change their eating habits if they are married or not. Head of
household being single was the base.
Considering that further literature suggest similar variables
used, we believe the variables we choose gave economic and logical
intuition onto meat eaters of the US. Half of our factors revolve
around age and education, which directly correlate to income. These
regressors should show forth how people spend on meat, if they so
choose to. Increases in education and age should have a negative
relationship with meat expenditures, because intuitively people become
more aware of the effects of meat and how it translates to health,
therefore they should have a negative relationship due to people
spending less. Income should theoretically increase if our hypothesis
is correct that meat is a normal good. The other half of our
regressors are demographic based. Like we previously stated, there
might be factors explained by those variables that information could
be collected from. We also considered the size of the family
Some factors we believed did not have an effect were sex of the
head of household, smoking, and alcohol consumption. When we looked at
the Sex dummy, we noticed that even though the data was split roughly
50-50 male-female, that most of our data (1595 observations)
identified themselves as married. We concluded intuitively that since
most 48% of our observations fell under married, those families make
decisions together with all possible information from both partners.
Further tests prove our theory due to the sex variable being
insignificant at any relevant level.
We originally believed that smokers and drinkers had some sort of
relationship with meat expenditures. The intuition behind it was that
smokers and drinkers who participate in such recreational activities
may have a lower regard for their body and their overall health. We
expected to see higher levels of meat consumptions for those who
identified themselves as such. But after much consideration and
testing of models, we concluded that those variables were not required
to the model and did not offer any significant information useful to
someone who is trying to target meat purchases with advertising.

7. Below are the descriptive statistics of all variables we used in
our model
Type of Variable Variable Paramaters of Variable Observations Percentage Mean Standard Deviation Minimum Maximum
Meat (with Zeros) Total Meat Any total Meat Purchases 3261 1.000 16.05 27.88 0.00 535.47
Beef Any beef purchases 3261 1.000 4.46 11.69 0.00 228.50
Poultry Any poultry purchases 3261 1.000 3.45 8.35 0.00 250.00
Pork Any pork purchases 3261 1.000 3.15 7.64 0.00 157.10
Seafood Any seafood purchases 3261 1.000 2.61 7.92 0.00 137.10
Other Meat Any other meat purchases 3261 1.000 2.39 6.88 0.00 141.37
Meat (with non-Zeros) Total Meat Any total Meat Purchases 2035 0.624 25.72 31.48 0.64 535.47
Beef Any beef purchases 974 0.299 14.92 17.37 0.65 228.50
Poultry Any poultry purchases 1028 0.315 10.95 11.78 1.06 250.00
Pork Any pork purchases 958 0.294 10.71 10.84 0.75 157.10
Seafood Any seafood purchases 702 0.215 12.12 13.26 0.74 137.10
Other Meat Any other meat purchases 955 0.293 8.15 6.88 0.00 141.37
Log of Meat Ltotmeat Any total Meat purchases 2035 0.624 2.80 0.96 -0.44 6.28
lbeef Any beef purchases 974 0.299 2.37 0.76 -0.43 4.43
lpoultry Any poultry purchases 1028 0.315 2.12 0.69 0.05 5.52
lpork Any pork purchases 958 0.294 2.08 0.73 -0.44 5.05
lseafood Any seafood purchases 702 0.215 2.11 0.86 -0.31 4.92
lothmeat Any other meat purchases 955 0.293 1.77 0.75 -0.45 4.95
Food ate at home foodhome All food at home purchases 3261 1.000 76.42 81.44 0.00 792.91
Food ate away from home foodaway All food away purchases 3261 1.000 45.52 64.13 0.00 939.07
Total Food Expenditures Foodtot All food purchases 3261 1.000 121.95 116.67 0.00 1,162.36
Age Teens age_ref<20 37 0.011 18.11 1.17 16.00 19.00
Twenties 20<age_ref<29 427 0.131 24.96 2.67 20.00 29.00
Thirties 30<age_ref<39 552 0.169 34.38 2.91 30.00 39.00
Fourties 40<age_ref<49 569 0.174 44.51 2.93 40.00 49.00
Fifties 50<age_ref<59 608 0.186 54.76 2.93 50.00 59.00
Sixties 60<age_ref<69 571 0.175 64.11 2.75 60.00 69.00
Seventies 70<age_ref<79 327 0.100 74.27 2.84 70.00 79.00
Eighties age_ref>80 170 0.052 54.67 3.02 80.00 87.00
Income fincaftm Disposable Income 3261 1.000 67,264.44 60,885.84 -321.00 585,949.00
lnfin Log of disposable income 3256 0.998 10.70 1.05 4.87 13.28
Location Urban Urban=0 3116 0.956 0.00 0.00 1.00 1.00
Rural Rural=1 145 0.044 1.00 0.00 2.00 2.00
Region Northwest region2=1 649 0.199 1.00 0.00 1.00 1.00
Midwest region2=2 736 0.226 2.00 0.00 2.00 2.00
South region2=3 1105 0.339 3.00 0.00 3.00 3.00
West region2=4 753 0.231 4.00 0.00 4.00 4.00
Education Highest Education level high_edu2 (0-16) 3261 1.000 13.70 1.71 0.00 16.00
somehighschl Edu_ref>11 398 0.122 10.47 1.57 0.00 11.00
hgschlgrd Edu_ref=12 707 0.217 12.00 0.00 12.00 12.00
somecllge Edu_ref=13 760 0.233 13.00 0.00 13.00 13.00
associate Edu_ref=14 250 0.077 14.00 0.00 14.00 14.00
bachelors Edu_ref=15 733 0.225 15.00 0.00 15.00 15.00
masters Edu_ref=16 413 0.127 16.00 0.00 16.00 16.00
Family Size fam_size From 1 to 9 3261 1.000 2.41 1.40 1.00 9.00
Race Wht Ref_race=1 2685 0.823 1.00 0.00 1.00 1.00
Black Ref_race=2 345 0.106 2.00 0.00 2.00 2.00
Native American Ref_race=3 11 0.003 3.00 0.00 3.00 3.00
Asian Ref_race=4 176 0.054 4.00 0.00 4.00 4.00
Hawaiian Ref_race=5 15 0.005 5.00 0.00 5.00 5.00
Mixed Ref_race=6 29 0.009 6.00 0.00 6.00 6.00
Class type inclass2 from 1 to 9 3261 1.000 6.71 2.41 1.00 9.00
Marital Status Married marital12=1 1596 0.489 1.00 0.00 1.00 1.00
Widowed marital12=2 304 0.093 2.00 0.00 2.00 2.00
Divorced marital12=3 498 0.153 3.00 0.00 3.00 3.00
Separated marital12=4 85 0.026 4.00 0.00 4.00 4.00
Never Married marital12=5 778 0.239 5.00 0.00 5.00 5.00

8. Below are the histograms of the 3 types of dependent variables we
used; Total meat (including no meat purchases), Total Meat (only those
who purchased meat) and the Log of Total Meat

9. Results
The other meat variables had similar distributions as total meat,
so those histograms were left out to save space since they have
replicated distributions. Below shows the relationship between total
meat expenditures compared to disposable income. It is shown that they
are clustered towards the bottom right hand corner, indicating that
some people do not purchase any sort of meat whatsoever. The plots
then to move outward and to the right, meaning with more income, there
are more meat purchases.
Since both total meat without zeros and log of total meat did not
include zeros in their variable, there were 1226 observations left
out. The breakdown of missing values for each non zero meat variable
and log meat variables is shown as below.
Type of Meat Number of Missing
Observation
Percentage of
Missing Values
Total Meat 1226 37.6%
Beef 2287 70.1%
Poultry 2233 68.5%
Pork 2303 70.6%
Seafood 2559 78.5%
Othmeat 2306 70.1%

10. These observations were left out of the models when they were
run. Since these were our dependent variables, the linear model did
not use them in any of its calculations of the coefficients. When
cleaning our data, we discovered that our disposable income variable,
fincaftm, has 5 observation where it was either zero or negative. This
was corrected when using it in the model by taking the log of income,
therefore knocking out those 5 observations.
Appendix A holds a table of the results from the three different
types of models used. They have been corrected for heteroskedasticity
by using robust standard errors. Multicollinearity was adjusted in the
variable Age_ref by removing observations lower than 26. R2, AIC/BIC,
Observations and dependent mean are followed at the bottom of the
table. These models differ slightly because they use oth as the base
of race, and therefore interaction dummies use oth*(region) as the
base. Appendix B shows different meat dependent variables to show
changes in particular types of meat. These were corrected for
heteroscedasticity and had no Multicollinearity by removing ages less
than 22.
The general results support our claims of total meat being a
normal good. In the first two cases, we see the log of income has a
positive coefficient, meaning that increases in income or more
specifically, the log of income, purchases of meat increase. In the
third case, there is a log-log relationship, meaning that the
coefficient, shows its elasticity. Since it is 0.0555, that means that
it’s not very elastic, meaning changes in price do have a strong
effect on meat purchases. We see this as a result of the UK Family
Survey, where they discuss how when incomes rise or prices fall, meat
expenditures don’t increase that much. Economically, this makes sense,
because people do not but more food that they don’t have to. We
conclude they will buy more meat, but it is not as large of a marginal
effect as other goods. Therefore it is still considered a normal good.
However, when breaking down different meats as the dependent variable
(appendix B), we see that beef, pork, and other meats have negative
coefficients, meaning increases in income lead to decreases in
purchases for those individual meats. However, in all 6 cases, the p
value of the log of income is 0.70 or greater, meaning that we do not
have enough evidence at any significant level to accurately say that
meat is a normal good. We can say that it is normal but it is not
supported by the data from the Consumer Expenditure Survey.
Another hypothesis was discovered correct, and that is regions
have a non-linear relationship, and that total meat purchases change
through-out regions. Further analysis shows that people living in the
west tend to eat the most meat out of any of the 4 regions. People in
the West on average spend $8.91 more on meat than those in the
Northeast, the base of our model. Those living in the South and
Midwest eat approximately $1.09 and $4.29 more, respectively, than
people living in the Northeast.

11. We also discovered a non-linear relationship with race as well.
When breaking down race into different dummies (appendix B) we see
that Mixed and Hawaiian people actually made up the highest spenders
on meat, given they spend $18.21 and $5.21 on total meat,
respectively. However, we decided that even though these give good
insight, that the number of observations that identify themselves as
that race were scarce. Therefore, as previously stated, the other non-
white and non-black races were grouped into one race, oth. Doing this
gives us more robust coefficients and more accurate results. When
using that as the base, we see that African Americans actually have
the highest coefficient, spending $6.50 more than other races and
whites spending only $0.57 more on meat than other races.
We took it a step further and created interaction dummies that
looked at different races in different regions to see where
advertising should best be properly allocated. We decided in doing so
that it would be best to use the oth race dummy variable for gaining
more concrete results as well as convenience. The results show that
other races tend to eat more meat the West and Midwest, while African
Americans eat the most total meat in the Northeast and South.
Considering the large make up of what is considered the “West” in the
United States, there are large populations of non-white and non-black
citizens living in that part of the country. Meat companies would be
best suited to advertise to non-white and non-black citizens in the
West and Midwest regions. Even though combined, non-whites make up
less than 17.7% of the data, this combined group spends more on meat
on average than whites do. It would be highly suggested to invest in
advertising towards them, even though total meat isn’t very elastic.
The following chart breaks down the highest and lowest levels of
consumption for each race broken up across regions (note none of the
interaction dummies are statistically significant). Highest indicates
the highest coefficient on all 4 interactions dummies, and lowest
indicated the lowest coefficient.
Interestingly enough, there is a pattern of eating patterns based
on the chart. Whites and black tend to live in the same regions where
a certain food is prevalent, and in that same region it is lower for
the other races. Take beef for example, where the Northeast has the
highest coefficients for beef for whites and blacks, while it’s the
lowest for other races. Conversely, the Midwest is the lowest region
for whites and blacks to consume meat, where it is the highest for
other races. Note, how the Midwest is the lowest for beef, poultry,
and other meats for whites and blacks, while the south is the region
Beef Poultry Pork Seafood Other Meat
Race Highest Lowest Highest Lowest Highest Lowest Highest Lowest Highest Lowest
White NE MW South MW MW West South West South MW
Black NE MW West MW MW NE NE West South MW
Other MW NE MW South West MW West South MW South

12. where poultry seafood, and other meats for other races. Even though
the p values on all of the interactions dummies is greater than 0.10,
we cannot accurately say that this is how the break down is, however
it is possible that this data can give us some insight.
Looking at individual models that use the logs of each meat
variable as the dependent, we were able to find the elasticities of
all of the different types of meat in our dataset. As previously
stated, total meat has an elasticity of 0.0555, meaning it is not very
elastic. Beef, on the other hand, was found to be -0.064, having a
negative coefficient further reinforces out statements that beef is
not a normal good. Poultry has a coefficient of 0.0064, meaning that
it not very elastic, but positive with income. Pork had a similar
effect as beef, with a coefficient of -0.047 being negatively elastic.
Seafood saw the same result of poultry, and having the highest
elasticity of 0.157. Other Meats, as unexpectedly, had a positive
elasticity of 0.055, even though earlier test proved it not to be a
normal good.
We believe our model to be an accurate representation of meat
consumption. Even though a solid majority of the variables were
insignificant and any relevant level, we attribute this to a variety
of food options available to the public, giving people the choice to
eat what they prefer. There has also been increased awareness to
health risks in recent years, as well as multiple diet trends that
have change people’s spending patterns and eating habits. Even though
the variables aren’t statistically significant, we believe that we
have chosen the right determinates that will accurately depict how
meat expenditures are spent in the United States.
In terms of looking at the signs of the coefficients of the
independent variables, we received what we expected. The signs of
income make sense once intuition is brought into the mix. Total meat
should increase as income increases because meat as we discovered is a
normal good. If they have more available income, they may choose to
spend more on meat. We were somewhat surprised that beef had a
negative coefficient on the log of income, due to we assumed people
tend to eat more steak as well as other fancy beefs as income rises.
One surprise that came to us was how old people in their sixties,
seventies and eighties had such high coefficients, when we expected
them to be much lower to avoid health risks. We considered the older
you are, the more disposable income you have due to not paying off car
and house loans, as well as already having most of their clothing
purchased, so they can have more money available to spend on meat. It
is also a possibility that the older they are, the more people they
are providing for, and so they could be spending their money on others
for meat (although we found as family size increases, expenditures on
meat decrease, so this possible assumption could not be true). Another
possibility for this is that old people may be set in their ways and
may not be as susceptible to change as younger people are. Trends such
as yoga, vegans, and vegetarians may not catch up to older people like
it does to younger people.
Something that kind of struck us by surprise is how increases in
education do not directly lead to decreases in meat expenditures as we

13. originally believed. We thought that the more educated someone is, the
higher their awareness of health risks of food in general increase.
However, due to the high correlation between education and income (36-
38% in our model), it’s possible it harkens back to the greater
disposable income and availability to purchase more meat goods. We do
recognize the negative coefficient on high_edu2, which measures
highest education in the house hold, and how the coefficients on
education decrease after obtaining their associates degree. Those
facts do expect to see. We had some thoughts on regions but really had
nothing set in stone in terms of expectations. When we saw higher
total meats in the West and South, we investigated to find out why
they were like that using interaction dummies.
There wasn’t a whole lot in this model that really caught us off
guard. When reviewing all of the coefficients in the model, we thought
about do they make sense intuitively and economically. Anything that
we found as unexpected we were able to justify using logic and
thinking.
Our major hypothesis test, H0: Bincome or Blogincome =0
H1: Bincome or Blogincome >0
After running the model, playing with the variables and settling
on a final model, we discovered that the p value of log of income
<0.78 15, , we can say at any relevant level that we fail to reject H0;
There is not enough evidence to say that income has a positive effect
on meat expenditures and it is a normal good.
Other hypothesis include; H0: BFamily Size =0
H1: BFamily Size >0
With a p value of 0.013, we can confidently say at any relevant
significance level that we can reject H0, there is enough evidence to
say that the number of people in a household affects how meat
expenditures are purchased for a home.
In looking a food spent at home, H0: BFoodhome =0
H1: BFoodhome >0
With a p value of <0.001, we can confidently say at any relevant
significance level that we can reject H0, there is enough evidence to
say that the amount of food spent at home has an effect of how much
meat a home buys. Most if not all of the dummy variables used in the
model were not statistically significant at any relevant level,
meaning the results from the model cannot confidently be presented
because there is not enough evidence to support those claims. Even
though we believe the model to be accurate, most of the results
presented cannot be confidently assured by the data and any confidence
level.
Diagnostics
In checking for errors that could have occurred, we tested for
heteroscedasticity, Multicollinearity, and any outliers as well as
looking at leverage and seeing how they had an effect. First, we
looked at heteroscedasticity, which we found in almost all of our
variables. This means there were some sort of change in the errors as

14. the dependent variable changes. Those were corrected by using HCC and
robust standard errors. We also ran into an issue of
Multicollinearity, where only the age_ref variable had some sort of
relationship with the other independent variables. We believe this was
due to the age dummies included in the model, however it’s also
possible that it could have come from somewhere else, such as income
or education. We corrected for this by removing observations that were
younger than 22. This would also remove large amounts of errors due to
young people who could have graduated college, possibly doing other
sorts of work.
We tried to remove high levels of leverage but those observations
didn’t lower Multicollinearity and didn’t change our coefficients by
enough to where it drastically changed anything. There were always 3
outliers in our project that through off our results, they were
removed from the models. The outliers typically have extreme levels of
food at home and foodaway from home, throwing off the data to the
point where the averages of those variables and the dependent means
decreases by a significant amount for being only 0.00092% of the data.
Inaccurate estimate would have stemmed from that, as well as
misallocation of the interaction dummies, making one race in one
region look significantly higher than how much they actually are.
Conclusions
After looking through all the variables included in the Diary of
the Consumer Expenditure Survey, we decided to look into how meat
expenditures varied across different demographics, regions, and if
meat as a whole is a normal good. Through careful cleaning of the data
and the model selection we chose to conduct, we arrived at the
conclusion, although statistically insignificant, that meat as a whole
is a normal good, while different types of meat are inferior while
others are normal. We also saw the non-linear relationship between age
and meat expenditures, as well as how different races enjoy different
meat. African Americans had the highest levels of overall meat
consumption, when only considering 3 types of people. Otherwise, mixed
people would have the highest consumption.
We then investigated which regions had the largest consumption of
any particular type of meat, and discovering that most meat
expenditures come from the West. To take it a step further, race of
people in each region has the highest meat consumption was
investigated. From there it was determined which regions to allocate
advertisements by breaking down those regions and looking at the
elasticities of each type of meat. There was an investigation of
education, and finding out that more education leads to higher meat
consumption, but marginal gains decrease as education increases past
an Associate’s Degree. Even though our models independent variables
were mostly statistically significant, and there isn’t a whole lot of
inference that can be done because of this, the results still give go
insight to the total meat expenditures in the United States.

15. Appendix
A.) 3 models used in general results
Parameter
Total
Meat
with
Zeros
Total
Meat
with
Non
Zeros
Log of
Total
Meat
AGE_REF -0.070 -0.101 -0.001
(00.054) (00.095) (00.003)
FAM_SIZE -0.873 -1.698 -0.001
(00.368) (00.572) (00.013)
FOODAWAY -0.005 -0.003 -12E-5
(00.008) (00.011) (00.000)
FOODHOME 00.242 00.280 00.006
(00.031) (00.036) (00.000)
Intercept 10.338 17.658 01.569
(9.015) (13.106) (00.476)
MW 4.285 11.688 00.265
(03.164) (06.862) (00.253)
South 1.086 -0.374 -0.119
(2.473) (03.643) (00.148)
West 8.907 13.237 00.076
(04.292) (06.777) (00.125)
Associate 01.789 01.543 -0.005
(01.544) (02.415) (00.089)
Bachelors 01.483 01.172 -0.069
(01.695) (02.511) (00.085)
Black 07.649 08.467 -0.051
(03.453) (05.724) (00.159)
Blackmw -9.165 -16.25 -0.255
(05.499) (09.052) (00.296)
blacksouth -3.671 -1.784 00.089
(04.011) (06.726) (00.206)
Blackwest -16.04 -19.55 -0.059
(05.499) (08.293) (00.220)
Divorced -0.836 -0.439 -0.064
(00.770) (01.278) (00.055)

16. Parameter
Total
Meat
with
Zeros
Total
Meat
with
Non
Zeros
Log of
Total
Meat
Eighties 04.032 05.380 -0.085
(03.315) (05.523) (00.186)
Fifties 01.954 02.462 00.142
(01.680) (02.724) (00.094)
Fourties 01.589 01.216 00.114
(01.236) (02.117) (00.078)
Hgschlgrd 01.424 02.037 00.009
(01.305) (02.087) (00.055)
high_edu2 -1.182 -1.515 -0.008
(00.434) (00.641) (00.019)
inclass2 -0.417 -0.548 -0.004
(00.400) (00.585) (00.021)
Lnfin 00.331 00.120 00.024
(01.014) (01.439) (00.051)
Married 00.832 01.900 00.076
(00.758) (01.208) (00.045)
Masters 01.055 01.283 -0.098
(01.971) (02.956) (00.101)
Othmeatb -0.244 -0.328 00.124
(01.355) (01.330) (00.030)
Porkb 05.786 05.679 00.427
(01.011) (00.992) (00.030)
poultryb 03.201 02.674 00.389
(01.489) (01.489) (00.031)
rural 01.158 01.135 -0.159
(02.128) (03.471) (00.070)
seafoodb 04.450 03.531 00.377
(01.734) (01.792) (00.031)
separated 01.041 02.240 00.004
(01.790) (02.873) (00.100)
seventies 04.567 06.193 00.113
(03.108) (05.004) (00.144)

17. Parameter
Total
Meat
with
Zeros
Total
Meat
with
Non
Zeros
Log of
Total
Meat
sixties 02.667 03.113 00.114
(02.438) (03.333) (00.117)
somecllge 00.718 00.090 -0.043
(01.197) (01.891) (00.064)
thirties 02.239 03.206 00.078
(01.168) (01.960) (00.070)
wht 0.574 -1.01 -0.054
(02.045) (03.110) (00.105)
whtmw -3.707 -10.83 -0.323
(03.334) (07.085) (00.257)
whtsouth 00.330 02.010 00.115
(02.599) (03.847) (00.154)
whtwest -10.103 -14.45 -0.158
(04.367) (06.855) (00.133)
widowed -1.782 -2.723 -0.074
(01.104) (01.828) (00.076)
R2
0.6051 0.5451 0.5767
AIC 18009 11583 -1688
BIC 18012 11586 -1685
Observations
Used in model 3126 1871 1871
Dependent
Mean 16.346 26.19 2.82
B.) Below is the output from all of the models we ran with
different meats as our dependent variable. All of these include zeros
in our model because we wanted to see how vegetarians affected meat
general meat purchases.
Total
Meat
Beef Poultry Pork Seafood Other
Meat
Dependent
Meat Mean
16.0519 4.4553 3.4530 3.1430 2.6106 2.3858
R2 0.6158 0.4670 0.4626 0.4868 0.4502 0.3718
Observations 3126 3126 3089 3126 3089 3126
AIC 17918 13571 11464 10751 11195 10736
BIC 17921 13574 11467 10753 11198 10739

18. Intercept 7.2248 6.8940 -1.9966 4.1455 -4.8961 3.0781
(8.8972) (4.8225) (3.1978) (2.8411) (4.0758) (3.1078)
Age Ref -0.0543 -0.0339 -0.0323 0.0042 -0.00098 0.0087
(0.0563) (0.0272) (0.0148) (0.0172) (0.0153) (0.0272)
Fam Size -0.7447 -0.4128 0.04049 0.0567 -0.2464 -0.1827
(0.3507) (0.1699) (0.0987) (0.1325) (0.1090) (0.1136)
Log of Income 0.7323 -0.1370 0.5265 -0.2051 0.6559 -0.1078
(1.0004) (0.5305) (0.4651) (0.3224) (0.4885) (0.3161)
Food at Home 0.2247 0.0754 0.0453 0.0367 0.0302 0.0370
(0.03294) (0.0145) (0.0133) (0.0086) (0.00513) (0.0086)
Food Away -0.0039 -0.0049 -0.0020 -0.0038 0.0039 0.0029
(0.0089) (0.0036) (0.0040) (0.00287 (0.0029) (0.0036)
Highest
Household Edu
-1.2461 -0.4870 -0.1275 -0.3463 -0.0204 -0.2648
(0.4489) (0.2129) (0.2404) (0.1348) (0.1175) (0.0900)
Beef Dummy 7.2175 11.3301 -1.4093 -0.9575 -0.8408 -0.9049
(1.0713) (0.5202) (0.4073) (0.3210) (0.3708) (-0.3169)
Pork Dummy 4.7721 -1.1087 -1.2280 8.9303 -0.8719 -0.9498
(0.9181) (0.4841) (0.3299) (0.2790) (0.3350) (0.3278)
Other Meat
dummy
-0.4087 -2.5746 -2.1260 -0.8018 -1.3342 6.4280
(1.2990) (0.6262) (0.7753) (0.3140) (0.2662) (0.2868)
Poultry Dummy 2.8724 (-2.788) 8.9687 -1.1090 -0.9283 -1.2694
(1.4347) (0.6731) (0.3700) (0.4696) (0.3011) (0.4555)
Seafood Dummy 4.3824 -2.7028 -1.4866 -0.8978 10.5849 -1.1152
(1.6812) (0.7631) (0.7673) (0.4931) (0.3742) (0.4816)
High School
Grad Dummy
1.2479 0.9369 -0.7873 0.5785 0.0443 0.47549
(1.2886) (0.6169) (0.4205) (0.4561) (0.3622) (0.3753)
Some College
Dummy
0.8907 0.2836 -0.6836 0.6952 -0.1581 0.7540
(1.1750) (0.5559) (0.4351) (0.4340) (0.3871) (0.3753)
Associate
Dummy
1.7144 0.5167 -0.4014 1.0347 -0.1671 0.4579
(1.4987) (0.6957) (0.5808) (0.5469) (0.5380) (0.3626)
Bachelors
Dummy
2.5045 1.1045 -0.6913 0.8240 -0.0004 0.8180
(1.7314) (0.8390) (0.6309) (0.5286) (0.5840) (0.4350)
Masters
Dummies
1.7759 0.5290 -0.3408 1.0347 -0.3008 0.8538
(1.9690) (0.9621) (0.8264) (0.6247) (0.7106) (0.4625)
Black Dummy 3.4677 0.9617 0.7711 0.5070 0.7481 0.4797
(0.9801) (0.4550) (0.3390) (0.2781) (0.3482) (0.2773)
Native
American
Dummy
-2.3619 -0.1462 -1.1511 0.3624 -0.5175 -0.9095
(2.5310) (1.2094) (0.9012) (0.5535) (0.7432) (0.6447)

19. Mixed Dummy 18.2182 8.5063 -0.6256 2.6875 4.5303 3.1196
(12.1018) (5.8937) (0.9979) (2.6755) (2.0553) (3.0840)
Hawaiian
Dummy
5.2749 1.7770 1.4983 1.2830 0.1755 0.5411
(2.9281) (1.8959) (1.2946) (0.5297) (0.8435) (0.6894)
Asian Dummy 2.6529 0.4614 -0.1910 1.2392 0.7389 0.4042
(1.0428) (0.4253) (0.3967) (0.3827) (0.4965) (0.5283)
Midwest Dummy 0.07826 -0.1864 0.0649 0.3850 -0.4177 0.2324
(1.0354) (0.4929) (0.3967) (0.3322) (0.3979) (0.2959)
South Dummy 0.8548 0.2977 0.2074 0.3555 -0.3206 0.3149
(0.7860) (0.4044) (0.2537) (0.2337) (0.3238) (0.2443)
West Dummy -0.3280 -0.2370 0.1860 0.2564 -0.6222 0.0888
(0.8533) (0.4520) (0.3011) (0.2326) (0.3638) (0.2775)
Thirties
Dummy
2.3767 1.3389 0.5930 0.2028 0.0671 0.1747
(1.1033) (0.5720) (0.3622) (0.2865) (0.3672) (0.4208)
Forties Dummy 1.4389 1.2374 0.2315 -0.1570 0.3325 -0.2055
(1.2394) (0.6081) (0.4468) (0.3930) (0.3879) (0.5479)
Fifties Dummy 2.2145 0.8753 0.8188 0.4259 0.1412 -0.0468
(1.7086) (0.7865) (0.4562) (0.5695) (0.4841) (0.8106)
Sixties Dummy 2.6042 1.4882 0.9541 0.4176 -0.3024 0.0466
(2.1102) (1.0542) (0.5437) (0.6621) (0.5846) (1.0359)
Seventies
Dummy
4.0063 2.2986 1.4568 0.2265 -0.4603 0.4847
(3.1301) (1.4805) (0.7058) (1.0208) (-0.4603) (1.3979)
Eighties
Dummy
3.7984 2.5365 1.3135 -0.4458 0.2019 0.1921
(2.1309) (1.7501) (0.8201) (1.0623) (0.9024) (1.5484)
Rural Dummy 1.5660 0.64678 1.3733 0.5619 -0.6756 -0.3399
(2.1309) (0.9529) (1.4184) (0.7357) (0.2899) (0.3292)
Inclass Dummy -0.6086 -0.0265 -0.2529 0.0296 -0.3358 -0.0229
(0.39489) (0.2128) (0.1606) (0.1174) (0.2091) (0.1240)
Married Dummy 0.6537 0.5583 -0.0814 -0.0956 0.5682 -0.2958
(0.7548) (0.3940) (0.3257) (0.2409) (0.2678) (0.2408)
Widowed Dummy -1.5268 -0.3993 -0.1655 -0.1888 0.3083 -1.08137
(1.1101) (0.5829) (0.3448) (0.3596) (0.3623) (0.3618)
Separated
Dummy
0.7363 0.00587 0.9480 -0.2490 0.1471 -0.1155
(1.8796) (0.8384) (0.8016) (0.3496) (0.4866) (0.6160)
Divorced
Dummy
-1.1315 -0.7429 -0.0638 -0.2167 0.3875 -0.5104
(0.7626) (0.3540) (0.2903) (0.2544) (0.3647) (0.2147)

20. All standard errors corrected for heteroskedacity. Only variable
with multicolineariy was age_ref, and it was corrected by removing age
of obs less than 22. This model in Appendix B did not account for
interaction dummies, partially because this model breaks down race
variables while the interactions grouped raced. However results were
found when more models were run including those interactions.