An Analysis Of Factors Affecting On Online Shopping Behavior Of Consumers
Keith Final Draft
1. Determinants of Consumer
Spending on Apparel
Keith Howard
12/15/2011
Paperpreparedunderthe directionof Dr.DavidCrary to fulfill part of the research requirement for the
M.A. in Economic Trade and Development at Eastern Michigan University
Thisresearchapplieseconometricanalysis todataobtainedfromthe Bureauof Labor Statistics
ConsumerExpenditure Surveyto testandquantifydemographicandeconomicdeterminantsof
spendingonapparel. The goal isto helpinvestorsandbusinessanalystsunderstandapparel salesgiven
specificeconomicand demographicdatafordifferentregionsof the UnitedStates. The findings
reinforce pastresultsonthe effectsof genderandincome onspendingonapparel;aswell asdiscover
some newsignificanteffectslike thatof populationdensityof locationandthe effectsof race as a
determiningfactor.
2. 1
Introduction:
The factors which determine apparel expenditure are sometimes assumed and sometimes
implied. It is a widely believed assumption that women spend more on apparel than men, as well
as the assumption that wealthier people tend to spend more on apparel1. What other factors are
included in determining how much a household or an individual spends on clothing and
accessories?
The topic I will be researching and analyzing are the determining factors that make up
consumer behavior pertaining to apparel consumption. I will use economic and demographic
information to compile a model that will determine an estimate of apparel expenditures for
individuals with different demographic and economic characteristics. The independent variables
I will use will include income, education level, age, race, gender, average hours worked, marital
status, and familial status. Many of these variables have been tested in similar studies described
below. My study will focus on the information obtained through the Bureau of Labor Statistics
(bls.gov) from their Consumption Expenditures Survey which gathers this information on
behavioral spending habits of individuals on a quarterly basis. This data can be used to help
understand the typical consumer’s spending on apparel. This can then be tailored for further
studies of specific geographic areas2. Abernathy et al discusses how current retailing strategies
include information technologies that analyze consumer sales data, and that this is the future of
the retail and apparel industries. Bram & Ludvigson suggest that analyzing consumer sentiment
“can help predict future movements in consumer spending.” My goal is to provide relevant
1 Heim states in his paper on consumer confidence and consumption spending, “If income declines…we expect
decliningconsumption.”So the adverse is also assumed.
2 The Consumer Expenditures Survey does not includedata on zip code, but demographic research compiled in the
future could investin obtainingthis information for a better fitof the model and assumptions.
3. 2
summaries of the determining factors of apparel expenditures. Then one could make
comparisons of different demographical areas to help decide where optimal locations would be
to invest in a store that primarily sells apparel. Past work has been completed to help show
determining factors of apparel consumption, but not to understand the consumption of apparel
given the above demographics.
Vieira (2010) and Pentecost and Andrews (2010) both conducted surveys where they
were fortunate enough to obtain data on fashion awareness. But, they also found that in annual
data, this was an insignificant factor3. These papers did not include potential important variables
that will be included in this research such as population density of the respondent’s region of
residence, race, and income. The data used in this paper will help further the knowledge on this
subject because it will build on earlier studies but use an expanded set of explanatory variables.
The data used in this paper is cross-sectional, compiled by surveys conducted by
employees of the Bureau of Labor Statistics, and is conducted on a quarterly basis over 5
quarters to substantially encompass all purchases made in a year. The data comes from the
Consumer Expenditure Survey (CEX) of 2010. These are the most recent data available from the
BLS4.
Literature Review:
Multiple studies have been written, with varying themes, regarding the determinants of
consumer expenditures on apparel. Some studies focus on sports apparel purchases, some focus
3 At the 5% level.
4 This data was made public for purchaseon September 27th.
4. 3
on adolescent spending on apparel, and some focus on online apparel shopping behavior.
Demographic and psychographic variables are taken into consideration in much of the research
(Scheerder, Vos, & Taks, 2011; Vieira, 2010; Beaudoin & Lachance, 2006; Sampson, 2009;
Pentecost & Andrews, 2009). Demographic information such as age, income, and education are
common throughout. A study compiled by Scheerder et al specifies expenditures on sports
apparel with included variables of “sport-related lifestyle characteristics” such as active sport
participation and sport spectatorship. This study continues with the theory that identification5
with a team, and not just active participation, is another determinant of sport apparel
expenditures. Similarly, it was found in a previous study by Wicker et al that a one hour increase
in weekly sport participation increases sport expenditures by €263 (roughly $354).
Work completed by Sampson shows that there is a growing trend of “Lifestyles Of
Health And Stability” (LOHAS) over the past decade. She contends that the growth of LOHAS
has come from a consumer who is becoming more socially and environmentally aware of the
impacts of the products one purchases. Her study focuses on the consumer’s purchasing
behavior of green apparel. Attention was paid to consumer’s knowledge of green apparel
companies, beliefs regarding the general environment, and attitudes towards green apparel,
among other variables contributing towards the purchase of green apparel (Sampson, 2009).
Awareness of a company’s intentions and practices can easily be compared to knowledge
of brand awareness. Brand awareness is another psychological determinant that contributes to
apparel expenditures (Beaudoin et al, 2006). Adolescents6, or Generation Y, pay more attention
5 This may be linked to one’s sense of belongingin a community. For example, someone who is not a loyal,or even
a casual sportfan,and is fromthe Detroit area - might purchasea Detroit Tiger’s hat to identify with the area they
are from. Itis my own theory that team logos are not only an identifier of fan-ship,butteam logos are also an
identifier of one’s loyalty to their city or region.
6 At the time of this paper, adolescents = Generation Y.
5. 4
to fashion than other generations (Beaudoin et al, 2006; Vieira, 2010; Pentecost et al, 2009).
This can be measured through expenditures on apparel compared to income of the individual,
and through behavioral surveys of one’s attitudes towards fashion. Where Sampson’s study
focused on variables such as knowledge of green companies, Beaudoin et al compares
psychological factors such as consumer confidence, self-esteem, and fashion innovativeness as
variables contributing to consumption behavior. Fashion is also seen as a status symbol,
especially with regards to high-end or designer apparel. Fogel et al found this to be a
contributing factor in designer purchases. This is similar to findings from the Beaudoin et al
study which includes self-esteem as a contributing factor.
It is well documented and theorized that gender plays a role in apparel consumption
(Scheerder, Vos, & Taks, 2011; Vieira, 2010; Beaudoin & Lachance, 2006; Sampson, 2009;
Pentecost & Andrews, 2009). Females have a higher awareness of brands (Beaudoin et al,
2006), fashion trend awareness (Vieira, 2010), and frequency of purchases (Pentecost et al,
2009). Pentecost et al found that in a three week period, frequency of female apparel purchases
were 71% to males’ 29%. So it would be wise to assume that gender has to be a variable of any
model describing or forecasting apparel consumption.
Vieira and Pentecost et al both theorize that fashion awareness7, is an important
determinant that should be included in any model. Fashion, as defined in the papers above, is a
basic term for apparel that follows current trends in the culture. Pentecost et al found that that
fashion fanship was a significant positive factor of apparel expenditures weekly, and monthly.
But adversely found that it was not statistically significant (p>0.05) when regressed with yearly
expenditures (Pentecost et al, 2009). Vieira hypothesized that materialism played a significant
7 Vieira denotes this as “fashion apparel buyingbehavior”;whilePentecost et al refers to this as “fashion fanship”.
6. 5
role in differentiating those who buy fashion apparel and those who do not. It was found that
materialism had no significant effect on fashion expenditures (Vieira, 2010).
Data for these studies was typically collected by surveys given to college students
(Vieira, 2010; Sampson, 2009), high school students (Beaudoin et al, 2006), surveys randomly
given to people at popular malls (Pentecost et al, 2009), and in one case, primary and high school
students took home surveys to their parents (Scheerder et al, 2011). A survey is the best method
for this type of study because the data that is to be tested needs to include individual
demographical and psychological factors as discussed above.
One disadvantage to the above studies is that a few were given exclusively to younger
generations. This can create some bias based on the hypothesis that I hold that older generations
are going to behave differently given apparel spending habits. Another study had responses only
from parents and guardians of school-age children (Scheerder et al, 2011), which only represents
people who have children; and totally excludes the spending habits of people without children
(who have more disposable income!) The study completed by Pentecost et al was given to
random people at malls, and this is closest to a random sample of the population. But one thing
stands out in this method: the survey was only given to people at malls – who obviously have a
predisposition to spending on apparel! It is not a good estimation of the population at large.
That is why I will use the Consumer Expenditure Survey, which I will discuss later. Also,
research completed on spending habits of online apparel consumers found that they were a
different type of shopper. The online shopper, (who is referred to as a hedonistic, recreational
shopper) gets just as much pleasure from shopping online as in malls or brick-and-mortar8
8 Brick-and-mortar refers to physical retail establishments.Click-and-mortar retailers arethosethat have online
shopping,as well as physical retail stores.
7. 6
venues (Cowart & Goldsmith, 2007). All types of shoppers are captured in the Consumer
Expenditure Survey, not just people who shop in face-to-face contact with retailers.
My dependent variable will be consumer spending on apparel for an average quarter9.
This is similar to the research compiled previously which I have discussed above. But most of
the abovementioned research had different timeframes in which the total apparel spending was
measured. The major differences will be in the explanatory variables I will use. Though I am
restricted to using socio-economic data, my data is better suited to making good assumption of
the national population because it is a much more diverse random sample of consumers in the
United States. I am missing some important qualitative variables such as fashion-awareness,
brand awareness, and self-esteem10, which I would theorize would have some effect on the
dependent variable. The research completed in this paper will include the largest data set of any
of the papers found relating to this same subject. The Consumer Expenditure Survey captures
data from almost 150,000 respondents, of which I have broken into 2 groups of interest: Married
couples and their family members living in their household; and single individuals living on their
own with no dependents in their households.
Theoretical Model:
The dependent variable in my model will be quarterly spending on apparel. The
explanatory variables will be income, education level, age, race, gender, employment status,
marital status, and familial status. I believe these variables determine a household’s and an
9 The Consumer Expenditure Survey takes randomsurveys of respondent’s spendinghabits coveringeach quarter
of the calendar year.
10 Though Pentecost et al found similar explanatory variables insignificant.
8. 7
individual’s habits of spending on apparel. The models use variables that have been found to
have significance in explaining spending on apparel, such as: income, age, and gender. I will
also add familial status, average hours worked per week, education, and race because these
socio-economic factors, I believe, will each influence spending in their own ways. Someone
with kids will increase their spending on apparel, someone who is employed will have a positive
effect on spending, and race and education may have something to do with spending – but I will
have to determine the effect after the data is analyzed (to see if there is any significance of those
variables in the model).
Single Person Model:
Ylog_tot_apparel = β0 + β1Xmega + β2Xmetro + β3Xcity + β4Xtown + β5Xrural + β6Xprimary + β7Xsome_hs +
β8Xdiploma + β9Xsome_coll + β10Xassoc + β11Xbach + β12Xmasters + β13Xphd + β14Xwhite + β15Xblack +
β16Xasian + β17Xpac_is + β18Xmulti_r + β19Xage_ref + β20Xage_ref_sq + β21Xfemale + β22Xlog_fsalaryx + µ
Married Household Model:
Ylog_tot_apparel = β0 + β1Xmega + β2Xmetro + β3Xcity + β4Xtown + β5Xrural + β6Xprimary + β7Xsome_hs +
β8Xdiploma + β9Xsome_coll + β10Xassoc + β11Xbach + β12Xmasters + β13Xphd + β6Xsp_primary + β7Xsp_some_hs
+ β8Xsp_diploma + β9Xsp_some_coll + β10Xsp_assoc + β11Xsp_bach + β12Xsp_masters + β13Xsp_phd + β14Xsp_white
+ β15Xsp_black + β16Xsp_asian + β17Xsp_pac_is + β18Xsp_multi_r + β19Xage2 + β20Xage2_sq + β21Xfemale +
β22Xlog_fsalaryx + µ
Variable Description:
mega: dummy variable, MSA larger than 4 million
metro: dummy variable, MSA 1.2 – 4 million
city: dummy variable, MSA 0.33 – 1.19 million
town: dummy variable, MSA 125 – 329.9 thousand
rural: dummy variable, MSA less than 125 thousand
“sp_” before these variables denotes spouse characteristic
primary: dummy variable, only primary school completed
some_hs: dummy variable, some high school completed
diploma: dummy variable, high school gradute
some_coll: dummy variable, only some college completed
9. 8
assoc: dummy variable, associate’s degree
bach: dummy variable, bachelor’s degree
masters: dummy variable, master’s degree
phd: dummy variable, phd
“sp_” before these variables denotes spouse characteristic
white: dummy variable, respondent is white
black: dummy variable, respondent is black
asian: dummy variable, respondent is Asian
pac_is: dummy variable, respondent is Pacific Islander
multi_r: dummy variable, respondent is multi-racial
“sp_” before these variables denotes spouse characteristic
age_ref: age of respondent
age_ref_sq: quadratic form of age_ref variable
female: dummy variable, 0 if male, 1 if female
log_fsalaryx: log form of annual salary of respondent/household
no_earnr: number of earners in the household
inc_hrs1: average number of hours worked by respondent
inc_hrs2: average number of hours worked by spouse of respondent
age2: age of spouse of respondent
age2_sq: quadratic form of age2 variable
perslt18: number of people in household under age of 18
fam_size: number of people in household
Expectations:
Using these models to understand the determinants of possible sales given specific
demographics will help investors decide where the best area will be for the store they want to
open. Also, changing demographics for the same area can affect the dependent variable in these
models. For example, imagine a store that has been in business in a small suburban
neighborhood that has grown to a more populous affluent community. The demographics of that
area would undoubtedly change as well, which would have an effect on the amount households
10. 9
and individuals will spend on apparel. This model can be easily modified to predict possible
apparel sales as well. But for the purpose of this paper, I am going to analyze the explanatory
variables.
I expect that the denser the MSA11 of the respondent, the more they will spend on
apparel. This variable captures part of the effect of fashion-awareness because people who live
in more populous areas tend to be more inclined to define themselves through their apparel12, and
therefore more conscious of their personal impression on other people. So, I hypothesize that as
the MSA gets smaller compared to the control variable of mega, less and less will be spent on
apparel.
H1: Xmega > Xmetro
H2: Xmega > Xcity
H3: Xmega > Xtown
H4: Xmega > Xrural
The variables for education and race will be interesting to look at because there is no real
theory behind whether they will increase of decrease spending. Though I hypothesize the
variable black will have a larger effect on apparel spending than the control variable white. This
could be because of a combination of two main factors: African-Americans are more likely to
live in urban environments13 and the African-American culture is more fashion-conscious14.
11 The term “MSA” will bediscussed in full in theData Description section of the paper.
12 This is anecdotal to the author.
13 i.e. Detroit is over 90% African-American.
14 Purely anecdotal.
11. 10
Hispanic origin of race is its own separate dummy variable15. Thus, the effect of this
characteristic cannot be captured in the race variable because Hispanic is not an option. Most
Hispanic people select white as their race in the normal race variable. So the effect of Hispanic is
captured in the race variable as white, more often than it will be captured in other race selections.
This could drive up the average purchases of apparel of white respondents (if my hypothesis that
Hispanic and black respondents spend more on apparel), and/or could drive down the average
apparel expenditures because the average income of Hispanic respondents is lower than the
average Caucasian white respondents. There is a possibility that the effects of these
characteristics offset each other, but probably not to the exact same amount.
H5: Xblack > Xwhite
I also expect that as age increases, spending on apparel will decrease. This is an easy
variable to predict because so much of the research discussed above has mentioned this and used
this variable in previous research. The variable female is also an easy variable to predict the
effect on apparel spending. This will have a positive effect on the dependent variable.
H6: Xfemale > 0
The amount of time a person works on average during a week will probably have some
effect on spending on apparel in a positive way. This variable affects apparel spending because
the more hours a person works, the more they will have to spend on apparel. It could be a
15 In the CEX, the racequestion was separatefrom the Hispanic origin question.
12. 11
confounding variable because of the correlation to income, but I feel it is important to keep in the
model because it helps capture employment status. Also, I will only use it in the household
model because in the individual model it will be highly correlated with income.
Because of the research complied above16, I will include the variable perslt18, which
captures the number of people in the household under the age of 18 that are not the respondent or
the spouse. I hypothesize that this will have an effect on apparel spending because the more
individuals in the household who are under 18 means more fashion-aware individuals who will
likely want to spend more on apparel (or at least spend more off their parent’s income on
apparel).
H7: Xperslt18 > 0
I also hypothesize that the more earners there are in the household model, the more
disposable income the family will have, which will lead to an increase in apparel spending.
Also, the more people in the household, I believe there will be an increase in spending on apparel
as well.
H8: Xno_earnr > 0
H9: Xfam_size > 0
16 Which found that younger people spent more on apparel than older people.
13. 12
Since my model has a logarithmic dependent variable, a co-efficient over 0 shows that as
that variable increases by one unit, and then we will see an increase in spending by that
percentage, ceteris paribus.
Also, previous economic theory suggests that as income increases, so will consumption.
This is represented by the following hypothesis:
H10: Xlog_fsalaryx > 0
Data Description:
The sample is a random survey completed by the BLS each quarter that captures all
purchases and demographic information for the survey respondents. This data is cross-section
data. The survey collects the expenditure data or each respondent for the past and current
quarters. I am using the data for one quarter of purchases for each respondent. The reason the
BLS collects data for each quarter is so that it can more accurately assess expenditures by
quarter, instead of trying to find respondents who keep records of their annual purchases.
The time period this data was collected ranges from the first quarter of 2010 through the
first quarter of 2011. It is compiled this way because the respondent is asked about the current
quarter and the previous quarter in each survey. Each respondent is surveyed for their purchases
per quarter, and the dependent variable reflects apparel purchases in the quarter in which the
survey was completed after. For example, if a survey was completed in Q1 of 2010, then the
respondent’s answers reflect Q4 of 2009.
14. 13
The Consumer Expenditure Survey collects similar variables as the research completed
previously, such as age, gender, and spending on apparel. But it does not collect qualitative data
that is very subjective such as brand awareness or fashion-awareness. Most of the variables are
qualitative demographic variables that are dummy variables. The continuous variables are
age_ref, log_fsalaryx, no_earnr, inc_hrs1, inc_hrs2, age2, perslt18, fam_size.
There are some non-normal distributions found in the summary statistics. These were in
the continuous variables fsalaryx and tot_apparel. The variable fsalaryx was only used if greater
than zero so a logarithmic representation could be made from this data. Logarithmic variables
were created for fsalaryx, and tot_apparel. These were named log_fsalaryx and log_tot_apparel,
respectively. Also, quadratic forms of age and age2 were created.
The variables that represent the population density of the respondent are mega, metro,
city, town, and rural. These represent the sizes of the Metropolitan Statistical Areas (MSA’s).
An MSA is a Primary Sampling Unit which is a group of counties which have not only
geographical connections, but economic connections as well. Simply put, it is a measured
characteristic of a small region in the United States, or metropolitan area. For instance, one of
the MSA’s (PSU’s) in the sample data is represented by the Detroit-Ann Arbor-Flint MSA. This
includes the counties within this triangulation of locations. In the sample data, there are 21
MSA’s (PSU’s) with a population greater than 1.5 million, which would put them into the metro
or mega descriptive variables depending on population size.
The apparel expenditures variable contains any spending on apparel from men’s socks,
women’s dresses, accessories17, kids shoes, to girl’s outerwear, men’s suits, women’s lingerie,
17 Such as gloves,sunglasses,scarves,ties,belts,etc…
15. 14
etc., etc. Basically anything a person can wear. In the CEX these can all be separated into their
own categories and measured; but for the purposes of this paper I have included them all together
to measure general apparel expenditures.
The typical respondent in the Single Person Model spends $505.17 per quarter on
apparel, lives in an MSA larger than 4 million18, is white19, has some college education20 (but no
degree), is 41.8 years old, female21, and has an annual salary of $41,467.65.
The typical respondent in the Household Model spends $874.01 per quarter on apparel, is
45.3 years old and has a spouse age 45, lives in a household of 3.7 people with 2 income earners
and 1.3 people under the age of 18, works an average of 41.2 hours per week with a spouse who
works an average of 41.3 hours per week, lives in an MSA of more than 4 million people22, has a
bachelor’s degree23 with a spouse who also holds a bachelor degree24, is white25 with a spouse
who is also white26, and with an annual household salary of $95,706.47.
18 37% likelihood
19 79% likelihood
20 27% likelihood
21 60% likelihood
22 41% likelihood
23 25% likelihood
24 26% likelihood
25 85% likelihood
26 84% likelihood
17. 16
Correlations are found in the Single Model as the level of education increases with salary.
For example, the correlation between education and income begins with no_sch at -0.0331, and
increases gradually to masters at .2128. Also from the Single Model, the correlation with
population density is strongest with mega and fsalaryx at .2522.
Econometric Results:
Regression was completed for both models and a few variables were found to be
insignificant in both. For the Single Person model these were inc_hrs127, no_sch and native28.
In the Household model the variables found to have low significance were age2, age2_sq29, and
perslt1830.
Also, White’s Robust standard errors we used to correct for any non-normal distributions
left after other measures had been taken. The final regression models are as follows:
Single Person Model:
Ylog_tot_apparel = β0 + β2Xmetro + β3Xcity + β4Xtown + β5Xrural + β6Xprimary + β7Xsome_hs + β8Xdiploma +
β9Xsome_coll + β10Xassoc + β11Xbach + β12Xmasters + β13Xphd + β15Xblack + β16Xasian + β17Xpac_is +
β18Xmulti_r + β19Xage_ref + β20Xage_ref_sq + β21Xfemale + β22Xlog_fsalaryx + µ
Married Household Model:
Ylog_tot_apparel = β0 + β2Xmetro + β3Xcity + β4Xtown + β5Xrural + β6Xprimary + β7Xsome_hs + β8Xdiploma +
β9Xsome_coll + β10Xassoc + β11Xbach + β12Xmasters + β13Xphd + β6Xsp_primary + β7Xsp_some_hs +
β8Xsp_diploma + β9Xsp_some_coll + β10Xsp_assoc + β11Xsp_bach + β12Xsp_masters + β13Xsp_phd + β15Xsp_black +
β16Xsp_asian + β17Xsp_pac_is + β18Xsp_multi_r + β19Xblack + β20Xasian + β21Xpac_is + β22Xmulti_r + β23Xage_ref
+ β24Xage_ref_sq + β25Xfemale + β26Xlog_fsalaryx + β27Xinc_hrs1 + β28Xinc_hrs2 + β29Xfam_size + β30Xno_earnr +
µ
27 T-stat of -0.91.
28 no_sch and native were found to be highly non-normal distribution and collinear,with lowt-stats.
29 age2 t-stat of -0.94, and age2_sq t-stat of 1.7 was so barely significantthatI dropped both from the model.
30 T-stat 0.45.
19. 18
I had previously expected that the denser the respondent’s population surroundings, the
more that respondent would spend on apparel. All of the coefficients for population are
negative. But, the two densely populated MSA-types are substantially less than the three
sparsely populated MSA-types.
Linear regression
Number of obs = 46168
Married Household
log_tot_apparel Coef. t-stat log_tot_apparel Coef. t-stat
metro*** -0.198951 -16.87 black*** -0.1417449 -3.21
city*** -0.1036346 -5.39 asian*** -0.2924466 -9.70
town*** -0.1933415 -15.43 pac_is*** -0.4509522 -4.63
rural*** -0.3486247 -18.51 multi_r* -0.0445095 -1.37
primary_sch*** 0.1930799 4.07 sp_black*** 0.1599659 3.58
some_hs*** 0.1057396 3.59 sp_asian* -0.0471836 -1.43
some_coll*** 0.1341075 8.57 sp_pac_is* -0.105139 -1.41
assoc*** 0.0656294 3.72 sp_multi_r* -0.0635888 -1.54
bach*** 0.1496439 9.69 age_ref_sq*** -0.0003361 -9.09
masters*** 0.0681685 3.88 no_earnr*** 0.0512558 5.87
phd*** 0.1550854 6.36 inc_hrs1*** 0.0044886 11.67
sp_primary_sch*** -0.1882662 -5.92 inc_hrs2*** 0.0025496 6.68
sp_some_hs*** 0.0903885 3.28 fam_size*** 0.0888853 21.27
sp_some_coll** 0.0296262 1.89 age_ref*** 0.0369061 10.98
sp_assoc*** 0.0663848 3.70 log_fsalaryx*** 0.2152439 24.48
sp_bach*** 0.126177 8.80 _cons 2.123106 20.99
sp_masters*** 0.1990366 11.08
sp_phd*** 0.2181962 7.87
Adj. R-squared = 0.1167
Root MSE = 0.9595
d.f. = 46164
significance at 10% * 1.645
significance at 5% ** 1.960
significance at 1% *** 2.576
20. 19
H1: Xmega > Xmetro Accept
H2: Xmega > Xcity Accept
H3: Xmega > Xtown Accept
H4: Xmega > Xrural Accept
In both the Household Model and the Single Person Model the variables for location are
found to be significant. In the Single Person Model, an individual who lives in an MSA that falls
into the metro category only spends on average about 5% less on apparel each quarter than an
individual who lives in an MSA that is considered mega. While individuals in less populous
areas spend 11% - 18% less on apparel, on average31. In the Household Model, the comparison
shows a starker contrast from mega to the smaller MSAs. The difference ranges from 10% to
35% less32, with the largest difference between mega and rural at 35% less spent on apparel, on
average.
I also expected that as the respondent’s education level increased, so would their
spending on apparel because of the implied higher income level. In both models, what is found
is that each higher level education variable increases spending on apparel. This may mean that
the education dummy variables are capturing part of the income of the respondents33.
I also expected that respondents who are black would spend more on apparel. And a
respondent who is black increases apparel (7.6%) compared with a white respondent, as well as
respondents who are Asian (13.6%).
H5: Xblack > Xwhite Accept
31 City = 18% less,Town = 11% less,Rural =16% less
32 Metro = 20% less,City = 10% less,Town = 19% less,Rural = 35% less
33 Refer to section on correlations above.
21. 20
The only proper way to test the female variable is to test only individuals, which is why a
model was created just for single people. The Single Person Model shows that females spend
much more on average than males: roughly 46% more34. This corresponds with previous
research discussed above regarding the spending habits of women compared to men.
H6: Xfemale > 0 Accept
The variable perslt18 was found to be insignificant with the lowest t-stat of roughly 0.45,
so it was left out of the final model. It had a co-efficient that increased household spending on
apparel by only 0.5% for each member of the household that was under the age of 18.
H7: Xperslt18 > 0 Reject
In the Household Model both no_earnr and fam_size were found to increase spending on
apparel, and both were found to be significant. As the number of earners in the household
increases, spending on apparel increases 5%. As the number of individuals in the household
increases, spending on apparel increases almost 9%.
H8: Xno_earnr > 0 Accept
H9: Xfam_size > 0 Accept
The co-efficient for log_fsalaryx in the Single Person model is 0.27, and similarly in the
Household model is 0.22; which both show very low income elasticity. This could be due to part
of the income effect being picked up by other descriptive variables, such as the slightly
34 With a very significantt-statof 22.87
22. 21
correlated variable mega and masters35. Also, low income elasticity is normal for necessity
goods36.
H10: Xlog_fsalaryx > 0 Accept
As inc_hrs1 and inc_hrs_2 increase by an average of 1 more hour worked per week, the
amount spent on apparel increases by 0.45% and 0.25%, respectively. This means if the average
hours worked per week by the respondent37 by 10 hours, spending on apparel will increase 4.5%
(which is probably due to correlation with the variable fsalaryx of .1876)
Summary
Research was completed on this subject because past research was very selective in the
sample of the population at large that was used to obtain data. The data used in this paper was
obtained from a very reliable source and gives a better representation of the U.S. population as a
whole. The assumptions made from the data are more indicative of the populations of question
that these models can be used for. This paper had the opportunity to focus on some interesting
hypotheses which the past research hadn’t completed. The hypotheses of population density and
race are the two most interesting to understand. Though sometimes small differences, the two
variables show a difference in priorities of cultures within our country. What exactly can be
inferred from these findings I will leave to sociologists and psychologists.
I believe the research I have completed uses the available data to make valid assumptions
of consumer behavior. The data is objective and contains relevant and current information. The
35 Correlations of 0.3103 and 0.2128, respectively.Both come from the Single Model and are the only two which
stand out in the correlation tables discussed earlier.
36 Income elasticity of demand states that positive income elasticity lower than 1 are usually dueto normal goods
that are also necessity goods – such as apparel.
37 Referring to inc_hrs1.
23. 22
missing variables which had been used in previous research, such as brand awareness and
fashion-ability, are subjective and hard to quantify in a fair and scientific manner. Who is to say
how fashionable they believe themselves to be on a scale of one-to-ten? But, if it were possible
to obtain this information from each respondent in a fair and objective way, this would have
much to do with the behavior of consumer’s spending on apparel38. The models used in this
research only received Adjusted R2 statistics of .144639 and .116740. Though these are very low
indicators41 the impacts of the variables used in each model are still significant, if not highly
significant. But more goes into the respondent’s choice of spending on apparel than can be
measured by basic demographics.
This analysis betters our understanding of the determinants that go into a consumer’s
apparel spending behavior. It furthers the research completed before that looked only at specific
groups of people and uses a data set that is much more representative of the United States
population as a whole. I find these results both internally and externally valid due to the
extensive nature of the Bureau of Labor Statistic’s Consumer Expenditures Survey.
The next steps that should be taken in continuing this research would be to create a more
in-depth character survey of the respondents. Being able to obtain data on fashion sense, brand
awareness, self-esteem, uniqueness, and trend awareness would help to further this research
immensely. But these are very subjective topics, and it will be hard to scientifically and
accurately gather this information. This research is beneficial to investors and business analysts
38 Contrary to pastresearch which found these variables insignificant. I think this was probably dueto some
sampling error.
39 For the SinglePerson Model
40 For the Household Model
41 Shows largevariationsin expenditures
24. 23
due to the valid assumptions that can be drawn from the data within, but more work can be done
to help complete a better model with more variables.
Summary Statistics:
Single Person OBS = 12509
Variable Mean Std. Dev. Min Median Max Skew Kurtosis JB Stat
tot_apparel 505.1699 1339.8440 1 285 38600 22.4650 628.2575 204816969.516
mega 0.3716 0.4833 0 1 0.5310 1.2824
metro 0.2408 0.4276 0 1 1.2120 2.4699
city 0.0552 0.2283 0 1 3.8960 16.1810
town 0.2091 0.4067 0 1 1.4300 3.0459
rural 0.1175 0.3220 0 1 2.3760 6.6455
no_sch 0.0024 0.0494 0 1 20.1637 407.5739
primary_sch 0.0132 0.1141 0 1 8.5358 73.8599
some_hs 0.0381 0.1914 0 1 4.8264 24.2942
diploma 0.1717 0.3772 0 1 1.7407 4.0299
some_coll 0.2668 0.4423 0 1 1.0543 2.1117
assoc 0.0944 0.2924 0 1 2.7747 8.6992
bach 0.2640 0.4408 0 1 1.0706 2.1461
masters 0.1152 0.3192 0 1 2.4110 6.8128
phd 0.0341 0.1815 0 1 5.1332 27.3503
white 0.7941 0.4044 0 1 -1.4544 3.1154
black 0.1291 0.3353 0 1 2.2129 5.8968
native 0.0052 0.0721 0 1 13.7164 189.1386
asian 0.0556 0.2292 0 1 3.8788 16.0451
pac_is 0.0014 0.0373 0 1 26.7161 714.7514
multi_r 0.0146 0.1201 0 1 8.0794 66.2768
age_ref 41.7969 15.8069 17 41 87 0.2368 1.9639 676.413
female 0.5985 0.4902 0 1 -0.4020 1.1616
fsalaryx 41467.6500 39911.6800 1 34000 279006 2.7197 15.0430 91013.491
log_income 10.1219 1.2215 10.4341 -1.2801 5.6315 7025.654
log_tot_ap~l 5.5817 1.1176 5.6525 -0.2254 3.9247 551.640
26. 25
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