BIBLIOGRAPHY
BAGAYAO, FILMER A., CALIZAR, BONIFACIO JR. G., LINGBAOAN,
RONALD T. and PALAO-AY, MYLA A. March 2009. Multinomial Logit Analysis on
Spending Behavior of Benguet State University Students. Benguet State University. La
Trinidad, Benguet
Adviser: DR. MARIA AZUCENA B. LUBRICA
ABSTRACT

Data were gathered and analyzed from 200 BSU students. The study aimed
specifically to determine the relationship of the type of spending behavior of students to
their socio-demographic, socio-economic characteristics and some of their expenditures.
The
result
of
the
study
revealed that the most variable that affect or has the stronger relationship to spending
behavior of BSU students was number of household members of the family. Monthly
income of parents/guardian, how often students receive their allowance, food expense
and educational attainment of parents/guardian showed a positive association. However,
there was a negative association of educational attainment to those who were not
consistent in following the set budget.









For age group, it implied a very weak association to the type of
spending behavior. Furthermore, rent expense and how often parents receive their income
revealed a weak association to the type of spending behavior. There was weak to
moderate association on the source of income of parents. The chances of students to

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strictly follow the set budget who were higlanders, ages 19 and below, males, course
under group III (BSAS, BSIT, BSES, BSND, BSA and BSET), first years to second
years, with 7 and above members of the family, source of financial support is family and
parents attained secondary or vocational were higher than those belonging to other
groups. In addition, there were higher chances to follow strictly the set budget of students
whose parents’ source of income is business, personal employment, private employee,
government employee, and receiving pension, receiving income in a daily and weekly
basis, receiving allowance in monthly, quarterly and not regular, monthly allowance of
3,001 and above, spend 1,501 and above, 3,500 and below, 1,000 and below, 1,500 and
below on food, rent, transportation and personal, respectively.


This study could come into conclusion that if a students’ family size is large there
is a big chance that he/she strictly follow the set budget.



Furthermore, the study recommended other studies related to spending behavior
of students. Other researches may use a larger sample size and include other variables
related to students spending behavior.

ii

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TABLE OF CONTENTS












Pages
Bibliography………………………………………………………………… i
Abstract …………………………………………………………………….. i
Table of Contents ……………………………………………………........... iii
INTRODUCTION

Background of the Study …………………………………………… 1

Statement of the Problem …………………………………………... 3

Objectives of the Study …………………………………………….. 3

Importance of the Problem …………………………………………. 3

Scope and Delimitation of the Study ……………………………….. 4
REVIEW OF RELATED LITERATURE ………………………………….. 5
THEORETICAL FRAMEWORK ………………………………………….. 11
METHODOLOGY
Respondents of the Study …………………………………………… 17

Instrument ……...…………………………………………………… 18

Data Analysis ………………………………………………………… 18

Definition of Terms …………………………………………………. 20
RESULTS AND DISCUSSIONS …………………………………………… 22
SUMMARY, CONCLUSION AND RECOMMENDATIONS ……………. 41
LITERATURE CITED ………………………………………………………. 44
APPENDICES ……………………………………………………………….. 46

iii

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1

INTRODUCTION

Background of the Study








Every student have their own way of dealing with other people these
behaviors will vary on their culture and personality manifested in their relationships
with their friends, family and community. This study focuses on the daily spending
behaviors of students in relation to their allowance, socio-demographic and socio-
economic
characteristics.


As Ago (2001) stated, “Money is the main source of living that tends to be
far from reach. This is true today and probably for the coming years and the
economic crisis that the world is experiencing. Though it is said that “Money is Not
Everything” or it cannot buy everything in this world, it is still a must for us to
sustain the budgetary requirement for our daily needs not only outside the school
but as well as in our studies. We also need to maintain a quality of living for a
better life.








Financial problems are true and very evident in our society. If not to all, to
most of us. It is a problem worthy of serious attention especially for a person who
wants to have a quality life. The path leading to financial success is full of
obstacles that must be overcome either by having a respectable financial asset or by
being able to budget your money efficiently to avoid useless expenditures.

It is not wrong to spend money as long as it is for needs and is not beyond
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our financial capacity. However, there are those who claim that their needs and
wants are not meet despite having enough resources. This is due to the
mismanagement of budget due to ill-advised expenditures and not knowing what to
prioritize.










Moreover, the lack of self-analysis and failure to face the problem, facts and
figures are two of the greatest causes of financial troubles. Lack of definite plan is
another; the analysis becomes useless unless it is followed with work-plan.

The desire of making it through college with limited resources but still able
to enjoy pleasures and quality life, takes firm determination and “will-power”. To
succeed it is not impossible for us to succeed while enjoying pleasures and quality
life at the same time with pleasurable pursuits and performing tedious and hard
task. We can say, therefore that poverty is not a hindrance to achieving success.
However, literacy is seen t be a life long process that needs not to be nurtured for a
long time and is deeply involved in social practices and tradition which is essential
to be successful and have a quality life.





The purpose of this study is to asses the effect of the socio-demographic,
socio-economic characteristics and some expenditure of Benguet State University
students on their type of spending behavior through multinomial logit analysis
technique. Multinomial logit analysis is used to analyze the relationships or
association of cross-tabulated nominal data. It allows the taking of standard
frequency cross-tabulation and find out which variables seem statistically most
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likely to be responsible for a particular effect. Nominal data are discrete
observations that can be sorted into categories. Adopting some log-linear
association determinants were used.

Statement of the Problem







This study dealt with the spending behavior of Benguet State University
students using multinomial logit model. This tries to answer the following
questions:









1.) What is the association between type of spending behavior of students
and some socio-demographic/economic variables?



2.) What is the association between type of spending behavior of students
and their expenditures?
Objectives of the study






Specifically, this study was conducted to: a) determine the association
between type of spending behavior of students and the socio-demographic and
socio-economic variables; and b.) determine the association between spending
behavior of students and their expenditures.





Significance of the Study







The results of this study would serve as a reference for students. It would
provide useful information to the parents/guardians to determine how much
allowance they should give to their children.
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Furthermore, the findings of this study will help improve our economy, for
example businessmen in would determine what kind of products or services to
produce based on the spending behaviors of students therefore avoiding a mismatch
on the students needs and their services.
Scope and Delimitation of the Study
The study concentrated mainly on the determining and analysis of the
spending behavior of college students in Benguet State University. This study is
delimited to ethnic origin, age, gender, course, year, no. of the household member
of the family, sources of financial support, educational attainment of
parents/guardian, sources of income of parents/guardian, how often parents receive
their income, average monthly income, how often students receive their allowance,
average monthly allowance, food expenses, rent expenses, transportation expenses,
personal expense and the type of spending habit of the students.



The study considered only college student of Benguet State University who
are presently enrolled this second semester of the school year 2008-2009.









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REVIEW OF RELATED LITERATURE
Kyrk (1933) presented two methods of arriving at cost of living of group in
society. 1) Through the studies of the cost if all items of expenditure and 2) through
budgets that show the goods and services used by the group.


Income is received us payment to property owned. Some people owned to
few resources to support what is regarded as minimum living in there community.
The return that this people obtain from the use of their resources that might have
been employed in their most protive uses does not provide them with sufficient
income to sustain of a living considered economically desirable as Bishop and
Toussaint
(1958)
quoted.


Villanueva (1981) stated that the income is one of the potent factors
influencing the pattern of consumption. As income increases, consumers want to
diversify their consumption by eating a wider variety of food, which they can now
afford
to
buy.


Paran (1981) claimed that respondents generating higher expenditure on all
items belonged to families receiving higher income, those having income of urban
wage and salary earning families increases faster than expenditure of physiological
necessities and slower that those for luxury. For farm families, they spend able
income increases much faster than either type of expenditures (Zimmer Man and
Black as Cited by Rivera, 1955).
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Cramer and Jenson (1979) stated that consumers spend everything they earn
on goods and services. Another is that consumers never seem to get enough of most
things. One of then stated reasons why consumers do not buy infinite quantities of
everything is that they have a limited amount of income to purchase clothing,
housing, foods, having haircuts and other things.




Valerio (1977) conducted a study on expenditure and income. The research
was intended to find out what are the sources of revenue of city of Baguio, as well
as the allocation of such revenue and attempt to analyze and compare the income
and expenditure of the city of Baguio, the expenditure pattern as observed by every
individual in accordance to his/her lifestyle. As bread earner he has to follow
certain pattern in his expenditure with his income in order for him to cope with his
financial problems to the basic needs necessities of life and whatever intentions he
may hold for his future (Alves, 1982 – 1983).




Food is one of the most components of living and its expenditure is almost
half of income of an individual. Indicates of this components are usually derived
from the result of households food consumption surveys (HFCS) and from food
balance sheet (FBS). In developing economies, these components of living can be
used as statistical framework in the circular process of planning and development
(Onate,
1964).


Dulay conducted another study in 1975. He compared the income and
expenditure of foreign students in the Philippines and determined the various
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revenues and the different appropriations of the funds for expenditure of said
province on a year-to-year basis.
Ago (2001), concluded in her study that most parents/guardian of her
respondents were farmer, most receive their allowance by monthly basis and their
highest expenditure goes to school fees, food and rentals. She also added that year
level have significant effect to cigar/liquor and school fees while age is highly
significant to food, snacks, cosmetics and outings.

Decoyna (2001), added that majority of the respondents generally derived
their financial allowance from their family.





Finally, students spend the biggest percentage of their money on foods,
clothing and shelter, transportation, recreation, and snacks. These items are prime
importance in the student’s community. Only four factors namely age, sex, civil
status, and parent’s income affect student’s expenditure (Gabriel, 1973).

Multinomial logit Model
The multinomial logit model is an alternative to full-profile conjoint
analysis and is extremely popular in marketing research (Louviere, 1991; Carson et.
al., 1994). In addition, Cramster (1998) state that a multinomial logit model is an
econometric or statistical model which is a generalization of logit models in which
there can be more than two cases.





Cramster added that multinomial logit regression is used when the
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dependent variable in question is nominal and consists of more than two categories.
Nominal variables are variables which consist of a set of categories which cannot
be ordered in any meaningful way. Stating an example that multinomial logit
regression would be appropriate when trying to determine what factors predict
which major college students choose .

Multinomial logistic regression involves nominal response variables more
than two categories. Multinomial logit models are multi equation models. A
response variable with k categories will generate k-1 equations. Each of these k-1
equations is a binary logistic regression comparing a group with the reference
group. Multinomial logistic regression simultaneously estimates the k-1 logits.
Further, it is also the case, that the model tests all possible combinations among the
k groups although it only displays coefficients for the k-1 comparisons.


Related Studies on the Application of
Multinomial Logit Model

Pundo and Fraser (2006) uses multinomial logit model to investigate the
factors that determine household cooking fuel choice between firewood, charcoal,
and kerosene in Kisumu, Kenya. Empirical results indicate that level of education
of wife, the level of education of husband, type of food mostly cooked, whether or
not the household owns the dwelling unit, and whether or not the dwelling unit is
traditional or modern type are important factors that determine household cooking
fuel choice. Implications for regional and national fuel policies are discussed.
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An observational study by Deb and Trivedi (2004) using mixed multinomial
logit model to model the enrollees' choice between health plans, each plan being
treated as a bundle of attributes formed from restrictions on provider access. The
results shows that enrollee and insurer reports of the attributes of enrollees health
plans are quite different, suggesting a dissonance arising, perhaps, from poor
information dissemination on the part of health plans and/or lack of attention on the
part
of
enrollees.


Fritsma and Grove (2005), used multinomial logit model to model the
relationship between education level and employment status, where employment
status is measured as part-time employed, full-time employed, unemployed, and out
of the labor force. They present both the coefficient estimates from the multinomial
logit models as well as the odds.





Dominique (2003), in his paper he used the K-deformed multinomial logit
model to study product differentiation. The focus is on the economic interpretation
of the deformation parameter which is the key parameter of the model. Then he
establishes the relationship between the parameter and probability choice, price
elasticity
and
mark
up.

Study on the effects of commuting and demographic variables on the
amount and distribution of out shopping were modeled using household-level
survey data in which the proportion of expenditures within specific categories of
goods were reported across neighboring retail market areas. The effects on the
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propensity to shop outside the core study area were estimated using the two-limit
tobit and logit models. Influences on the relative distribution of that out shopping
were modeled by multinomial logit. The multinomial logit and tobit models were
shown to produce similar estimates, with empirical results indicating that retail
sales leakages are increased for out commuters for certain types of goods, Burkey
and
Harris
(2003).


Lymp, et al (2003) studied a choice-based conjoint in economics and
marketing to assess the relative contributions of various product attributes and to
predict consumer behavior. In their study, subjects were given a set of questions.
Each question is a scenario containing several choices from which the subject
makes a selection. The choices are characterized by various attributes and particular
levels of the attributes. Based on these stated preferences of the subjects, inference
is made on the effects of various attributes and their levels on decision making.
They used multinomial logit (MNL) model on this study. They develop the MNL
model for choice-based conjoint studies and relate the MNL model to well-known
bio statistical models. They also describe a Markov Chain Monte Carlo method for
adding random coefficients to the MNL model. How the MNL model can be
interpreted in terms of odds ratios and attributable risk estimates were discussed.
Random coefficients MNL model were also fitted to data from a choice-based
conjoint study on patient preference for tertiary care medical centers.



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THEORETICAL FRAMEWORK

Multinomial Logit Models
Regression models for the analysis of categorical dependent variables with
more than two response categories. Several of the models that we will study may be
considered generalizations of logistic regression analysis to polychotomous data.
We first consider models that may be used with purely qualitative or nominal data,
and then move on to models for ordinal data, where the response categories are
ordered.

The Multinomial Distribution
The multinomial distribution considers a random variable Yi that may take
one of several discrete values, which we index 1,2,…, J. Then let

(1)
Denote the probability that the i-th response falls in the j-th category.
Assuming that the response categories are mutually exclusive and ex-haustive, we
have
for each i, i.e. the probabilities add up to one for each
individual, and we have only J -1 parameters.






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For grouped data it will be convenient to introduce auxiliary random
variables representing counts of responses in the various categories. Let
denote
the number of cases in the i-th group and let Yij denote the number of
responses from the i-th group that fall in the j-th category, with observed value yij.
Note that ∑j yij = ni, i.e. the counts in the various response categories add up to the
number of cases.

For individual data ni = 1 and Yij becomes an indicator (or dummy)
variable that takes the value 1 if the i-th response falls in the j-th category and 0
otherwise, and
since one and only one of the indicators yij can be
`on' for each case.
The probability distribution of the probability distribution of the counts Yij
given the total ni is given by the multinomial distribution

(2)

The Multinomial Logit Model
We now consider models for the probabilities Пij . In particular, we would
like to consider models where these probabilities depend on a vector xi of
covariates associated with the i-th individual or group.



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Multinomial Logits
Perhaps the simplest approach to multinomial data is to nominate one of the
response categories as a baseline or reference cell, calculate log-odds for all other
categories relative to the baseline, and then let the log-odds be a linear function of
the predictors.
Typically we pick the last category as a baseline and calculate the odds that
a member of group if all in category j as opposed to the baseline as


Modeling the Logits
In the multinomial logit model we assume that the log-odds of each response
follow a linear model.

(3)
where αj is a constant and βj is a vector of regression coefficients, for j =1,2,…,J-1.
Note that we have written the constant explicitly, so we will assume henceforth that
the model matrix X does not include a column of ones.
This model is analogous to a logistic regression model, except that the
probability distribution of the response is multinomial instead of binomial and we
have J-1 equations instead of one. The J-1 multinomial logit equations contrast
each of categories 1,2,…J-1 with category J, whereas the single logistic regression
equation is a contrast between successes and failures.
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Note that we need only J-1 equations to describe a variable with J response
categories and that it really makes no difference which category we pick as the reference
cell, because we can always convert from one formulation to another.
The missing contrast between categories 1 and 2 can easily be obtained in terms of the
other two, since



(4)

Modeling the Probabilities
The multinomial logit model may also be written in terms of the original
probabilities ∏ij rather than the log-odds. Starting from Equation 3 and adopting the
convention that niJ = 0, we can write

(5)
for j = 1,…,J. To verify this result exponentiate Equation 3 to obtain Пij = ПiJ
exp{nij} and note that the convention niJ = 0 makes this formula valid for all j.
Next sum over j and use the fact that ∑j Пij = 1 to obtain Пij = 1/∑jexp{nij}.
Finally, use this result on the formula for Пij. Note that Equation 5 will
automatically yield probabilities that add up to one for each i.





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Maximum Likelihood Estimation
Estimation of the parameters of this model by maximum likelihood
proceeds by maximization of the multinomial likelihood (2) with the probabilities
Пij viewed as functions of the αj and βj parameters in Equation 3.

The Equivalent Log-Linear Model
Multinomial logit models may also be fit by maximum likelihood working
with an equivalent log-linear model and the Poisson likelihood.
Specifically, we treat the random counts Yij as Poisson random variables
with means μij satisfying the following log-linear model.

(6)
where the parameters satisfy the usual constraints for identifiability. There are three
important features of this model:
First, the model includes a separate parameter X’ίβ*j for each multinomial
observation, i.e. each individual or group. This assures exact reproduction of the
multinomial denominators ŋi. Note that these denominators are fixed known
quantities in the multinomial likelihood, but are treated as random in the Poisson
likelihood. Making sure we get those right makes the issue of conditioning moot.
Second, the model includes a separate parameter α*j for each response
category. This allows the counts to vary by response category, permitting non-
uniform margins.
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Third, the model uses interaction terms X’ίβ*j to represent the effects of the
covariates Xί on the log-odds of response j. Once again we have a `step-up'
situation, where main effects in a logistic model become interactions in the
equivalent log-linear model.

(7)
This equation is identical to the multinomial logit Equation 3 with αj =α*j-α*j and
βj = βj- βj. Thus, the parameters in the multinomial logit model may be obtained as
differences between the parameters in the corresponding log-linear model. Note
that the Өi cancel out, and the restrictions needed for identification, namely ŋiJ = 0,
are satisfied automatically.

(8)


















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METHODOLOGY

Respondents of the Study

The respondents of the study were college students of Benguet State
University-Main Campus. Out of the 6118 college students enrolled at BSU,
samples of 200 respondents were chosen to be the sample of the study using
Stratified Random Sampling. The population was subdivided into three
subpopulations called strata. Stratum I included students who are taking up B.S.
Education, B. Elementary Education, B. Library Science and B.S. Home
Economics. The students from these courses are classified as group 1. The B.S.
Forestry, B.S. Nursing, Doctor of Veterinary Medicine, B.S. Agricultural
Engineering and B.S. Development Communication constituted stratum II. Stratum
III composed of B.S. Applied Statistics, B.S. Information Technology, B.S.
Environmental Science, B.S. Nutrition and Dietetics B.S. Agriculture and B.S.
Entrepreneurial Technology. The numbers of sample students from each stratum
were determined using the proportional allocation with the given formula:


 
where
, Slovin’s Formula





N = the population size

Ni = subpopulation size of the ith stratum
e = margin of error
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Stratum I consisted of 1,632 students which is 26.68% of the total
population, stratum II with 2,039 students constituted 33.32% and 2,447 in stratum
III with 40% of the total population. Thus the numbers of respondents to be taken
were 53, 67 and 80 from stratum I, II and III, respectively. The sample of each
subpopulation was drawn using simple random sampling.

Instrument
The study utilized a questionnaire as the main instrument for data-collection.
It consisted of the respondents demographic profile which includes the following:
gender, age, regional origin, no. of household members of the family, sources of
financial support, educational attainment of the one sending them to school, and
how do they spend their allowance.

Data Analysis

The collected data were encoded in the computer and analyzed using
Statistical Packages for Social Sciences (SPSS). Multinomial logit analysis
techniques was employed on the data gathered from the respondents treating the
type of spending behavior of college students enrolled second semester year 2008-
2009 as the dependent variables and the socio-demographic, socio-economic profile
and some expenditures as the independent variables which include region of origin,
age, gender, course, year, no. of the household member of the family, sources of
financial support, educational attainment, sources of income, average monthly
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allowance, food expenses, rent expenses, transportation expenses, and personal
expenses.






































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Definition of Terms






Income. This is money income receive by a college student from various
sources.







Expenditure. This refers to the cash outlay incurred by a college student. It
only included regular expenditure.



Allowance. Money receive by student from their parents or guardians on
regular basis. It also included stipends of scholar students.

Dependent
Variable. Refers to the variable that is determined or explained
by one or more explanatory variables.



Independent
Variables. Refers to the variable used to predict values of the
dependent variable in regression
analysis.

Variable. Refers to the characteristics of interest, which is measurable and
observable in every aspect in study.
Multinomial Logit Model is a regression model which generalizes
regression logistic by allowing more than two discrete outcomes.

Gender. This refers to either male or female.
Respondents. This will refers to the students who will furnish the
information or answer the questionnaire.

Categorical Data. Data that consist of count of people, place as things
grouped in any system of classification.
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Definitely not following the set budget. These are students who are
overspending.
Not consistent on following the set budget. These are students who are
averagely spenders.
Strictly following the set budget. Students who are not overspending.

Valid. This indicates the number of observations in the dataset were the
outcome variable and all predictor variable are non-missing.
Intercept. This is the multinomial logit estimate for definitely not following
the set budget relative to strictly following the set budget, and Not consistent on
following the se budget relative to strictly following the set budget when the
predictor variable in the model are model are evaluated at zero.
Standard
Error. These are the standard errors of the individual regression
coefficients for the two respective models estimated.
Wald. This is the wald chi-square test that tests the null hypothesis that
estimate equals 0.




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RESULTS AND DISCUSSION




Relationship Between Type of Spending
Behavior and Regional Origin



Table 1 shows the distributions of students according to ethnic origin and
type of spending behavior. The odds ratio of .71 indicates that the chance of
highlander is lower than the chance of lowlander to spend outside the set budget
rather than strictly following the set budget. An odds ratio of .90 means that the
tendency of students from the lowlands to spend not consistently within the set
budget instead of strictly following the set budget is 1.11 times greater than the
chance of highlander students to fall in that type of spending behavior. The Yule’s

Table 1.
The Regional origin and type of spending behavior of the
respondents and the odds ratio and Yule’s Q values


ETHNIC
TYPE OF SPENDING BEHAVIOR

ORIGIN
Definitely not Not consistent Strictly

following the
of following
following the
TOTAL
set budget
the set budget set budget
Highlander
52 55 12
119
Lowlander
38 36 7
81
Total
90 91 19
200
Odds ratio (Yule’s Q*)



Highlander
.71 .17 .90 -.05 -

Lowlander
-
-
-

Legend: *Yule’s Q values in bold font:

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


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Q of .17 and -0.05 indicate that there is very weak association between ethnic
origin and spending behavior of students.

Relationship Between Type of Spending
Behavior and Age of Students


Table 2 shows the cross tabulation of age and type of spending behavior of
the respondents including the odds ratio Yule’s Q values. The Yule’s Q –.08 shows
that age 19 and below has very weak negative association with definitely not
following the set budget. The odds ratio .86 means that ages 19 and below
respondents have .86 more times chance of not following the set budget than
following the set budget compared to students aging 20 and above. The respondents

Table 2.
Age group and type of spending behavior of the respondents and the
odds ratio and Yule’s Q values

AGE OF THE
TYPE OF SPENDING BEHAVIOR

STUDENTS
Definitely not
Not consistent Strictly

following the
on following
following the
TOTAL
set budget
the set budget
set budget
19 and below
44
43
10
97
20 and above
46
48
9
103
Total
90 91 19
200
Odds ratio (Yule’s Q*)



19 an above
.86 -.08
.81 -.11 -

20 and above
- -
- - -

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association



Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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aging from 19 and below have very weak negative association with not consistent
on following the set budget. The odds ratio .81 means that the chance of aging 20
and above to be in the average of spending instead of not overspending is 1.23
times than the chance of 19 years old and below.

Relationship Between Type of Spending
Behavior and Gender of Students


The distributions of the type of spending behavior of the respondents are
shown in Table 3 the odds ratio and Yule’s Q values are also presented. There is a
weak association between the male and definitely not following the set budget. The
odds ratio .56 means that the chance of male respondents to definitely not

Table 3.
Gender and type of spending behavior of the respondents and the
odds ratio and Yule’s Q values



GENDER
TYPE OF SPENDING BEHAVIOR

Definitely not
Not consistent Strictly

following the
on following
following the
TOTAL
set budget
the set budget
set budget
Male 30 40 9
79
Female 60
51
10
121
Total 90
91 19
200
Odds ratio (Yule’s Q*)



Male
.56 -.28
.87 -.07 -

Female
-
-
-

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


Multinomial Logit Analysis on Spending Behavior
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following the set budget rather than strictly follow the set budget is smaller than the
chance of female respondents to follow that type of spending behavior.

Relationship Between Type of Spending
Behavior and Year Level

The distribution of year level and type of spending behavior with odds and
Yule’s Q values are presented in table 4. There is a weak negative association
between first to second year students and definitely not following the set budget.
The chance that first year to second year will definitely follow the set budget rather
than strictly follow the set budget is .61 than the chance of third to fourth years.
First year to second year in not consistent on following the set budget has a very.
weak association.


Table 4.
Year level and type of spending behavior of the respondents and the
odds ratio and Yule’s Q values

YEAR
TYPE OF SPENDING BEHAVIOR

LEVEL
Definitely not
Not Consistent Strictly

following the
on following
following the
TOTAL
set budget
the set budget
set budget
I and II
41
47
11
99
III and IV
49
44
8
101
Total 90
91
19
200
Odds ratio(Yule’s Q)



I and II
.61 -.24 .78 .78
-.12
-
III and IV
-
-
-
-
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association
Multinomial Logit Analysis on Spending Behavior
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Relationship Between Type of Spending
Behavior and Number of Household

The table on the number of household and type of spending behavior shows
that moderate relationship exists between the two variables. The tendency that
students with 6 and below members will be on the definitely following the set
budget rather than strictly following the set budget is 2.50 times than the chance of
students with 7 and above family members. In the chance to be not consistent in
following the set budget instead of strictly following the set budget is 2.94 times
greater than the probability of students with 1-6 family members to be in the not
consistent of following the set budget rather than strictly following the set budget.

Table 5.
Number of households and spending behavior of the respondents
and the odds ratio and Yule’s Q values
NUMBER OF
TYPE OF SPENDING BEHAVIOR

HOUSE-
Definitely not Not consistent Strictly

HOLDS
following the on following
following the

set budget
the set budget set budget
TOTAL
1-6 36 40 4
80
7 and above
54
51
15
120
Total 90
91 19
200
Odds ratio (Yule’s Q*)



1-6
2.50 .43 2.94 .49
-
7 an above
-
-

-
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association

0.8
≤|Q|<1.0 =very strong association





Multinomial Logit Analysis on Spending Behavior
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Relationship Between Type of Spending
Behavior and Source of Financial Support


The distribution of type of spending behavior and source of financial

support are shown in table 6. The odds ratio and Yule’s Q are also presented. There
is a weak association between the family and definitely following the set budget.
The odds ratio .51 which is less than one implies a negative association. The Yule’s
Q -.54 shows a negative moderate association between family and consistently
following the set budget.

Table
6.
Source of financial support and spending behavior of the
respondents and the odds ratio and Yule’s Q values

SOURCE OF
TYPE OF SPENDING BEHAVIOR

FINANCIAL
Definitely not Not
Strictly

SUPPORT
following the consistent in following the
set budget
following the set budget
TOTAL
set budget
Family 73 65 17
155
Grant/Scholarship 17 26 2
45
and Self
Supporting
Total 90
91
19
200
Odds ratio(Yule’s Q)



Family
.51 -.32 .30 -.54 -

Grant Scholarship - - -

and Self

Supporting
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association



Multinomial Logit Analysis on Spending Behavior
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Relationship Between Type of Spending
Behavior and Educational Attainment of
Parents/Guardian


Table 7 shows that the odds of respondents parents/guardian is under
elementary level in either definitely not or not consistent in following the set
budget is 1.18 and .75 times greater respectively than strictly following the set
budget over college level and degree holder. The association between respondents’
parents under elementary level and definitely not following the set budget is very

Table 7.
Educational Attainment of parents/guardian and spending behavior
of the respondents and the odds ratio and Yule’s Q values


EDUCATIONAL
TYPE OF SPENDING BEHAVIOR

ATTAINMENT
Definitely not Not
Strictly

following the consistent in
following the
set budget
following the set budget
TOTAL
set budget
Elementary 23
19
3
45
Secondary in
54 55 14
123
Vocational
College Level
13 17 2
32
and Degree
holder
Total
90
91
19
200
Odds ratio (Yule’s Q*)



Elementary
1.18 .08 .75 -.14 -

Secondary in
.59 -.26 .46 -.37 -

Vocational
College and
-
-
-

Degree holder
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association



0.8 ≤|Q|<1.0 =very strong association
Multinomial Logit Analysis on Spending Behavior
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weak association (.18), and in not consistent in following the set budget is very
weak negative association (0.14). The odds ratio 1.18 implies that the tendency of
students whose parents is elementary level to definitely not following the set
budget is almost the same to strictly following the set budget. There is both
negative weak association between being secondary and vocational and either
definitely not following or not consistent on following the set budget. As indicated
by the Yule’s Q value -0.26 and -0.37, respectively.

Relationship Between Type of Spending
Behavior and Source of Income of Parents
Guardian of the Respondents


As shown in table 8, the odds ratio .73 implies that the tendency of the
students o definitely not follow the set budget is greater than to strictly following
the set budget as compared to students whose parents source of income is farming.
The association between definitely not following and source of income is business
and personal employment is a negative very weak association. The odds ratio of
students whose parents’ source of income is pension, private employee or
government employee is .91 and definitely not following the set budget is almost
the same to students whose parents’ source of income is farming. The Yule’sQ-
0.39 and -0.33 implies a negative weak association between respondents whose
source of income is business and personal employee, pension, private employee
and government employee and not consistent in following the set budget.
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Table 8.
Source of income of parents/guardian and spending behavior of the
respondents and the odds ratio and Yule’s Q values

SOURCE OF
TYPE OF SPENDING BEHAVIOR

INCOME
Definitely not Not consistent Strictly

following the
in following
following the
TOTAL
set budget
the set budget set budget
Business and
24 24 6
54
Personal
employment
Pension,
55 49 11
115
Private and
Gov’t
employee
Farming 11
18
2
31
Total 90
91 19
200
Odds ratio (Yule’s Q*)



Business and
.73 -.16
.44 -.39 -

Personal
employment
Pension,
.91 -.05 .50 -.33 -

Private and
Gov’t
employee
Farming
-
-
-

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


Relationship Between Type of Spending
Behavior and How Often Parents Receive
their Income

In table 9, it shows that the odds of the students of the receiving allowance
daily and weekly in definitely not following the set budget is .51 the same students
who’s source of income financial support is farming. The Yule’s Q value -0.32
Multinomial Logit Analysis on Spending Behavior
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implies a weak negative association between students receiving their allowance
daily and weekly and definitely following the set budget. The association between
not consistent in following the set budget and with parents receiving their
allowance daily and weekly in following the set budget is weak negative.

Table 9.
How often Parents receive their income and spending behavior of
the respondents and the odds ratio and Yule’s Q values





HOW OFTEN
TYPE OF SPENDING BEHAVIOR

PARENTS
Definitely not Not consistent Strictly

RECIEVED
following the
in following
following the

THEIR
set budget
the set budget set budget
TOTAL
INCOME
Daily and
17 19 6
42
Weekly
Monthly and
73 72 13
158
Quarterly
Total 90
91 19
200
Odds ratio (Yule’s Q*)



Daily and
.51 -.32 .57 -.28 -

Weekly
Monthly and
-
-
-

Quarterly
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association









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Relationship Between Type of Spending
Behavior and Parents Monthly Income

As shown in table 10, the odds ratio 1.23 implies that the tendency of the
students who’s definitely not following the set budget is lesser than to those who
are not consistent in following the set budget. The association between definitely
not following the set budget and having a parent’s monthly income of 15,000 and
below is very weak association as indicated by the Yule’s Q value 0.01. The odds
ratio of students with parents’ monthly income is 15,000 and below in not
consistent in following the set budget is 1.51 times greater than definitely not

Table 10. Parents’ monthly income and spending behavior of the respondents
and the odds ratio and Yule’s Q values


PARENTS’
TYPE OF SPENDING BEHAVIOR

MONTHLY
Definitely not Not consistent Strictly

INCOME
following the
on following
following the
TOTAL
set budget
the set budget set budget
15,000 and
52 57 10
119
below
1,5001 and
38 34 9
81
above
Total 90
91 19
200
Odds ratio (Yule’s Q*)



15,000 and
1.23 .10 1.51 .20 -

below
15,000 and
-
-
-

above
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


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following the set budget. The Yule’s Q 0.20 implied a weak association between
students with parents’ monthly income of 15,000 and below and not consistent in
following the set budget.


Relationship Between Type of Spending
Behavior and How Often Students Receive
their Allowance

In table 11, it shows that the odds of the students receiving allowance daily
and weekly in definitely not following the set budget is 1.46 greater than strictly
Table 11. How often students received their allowance and spending
behavior of the respondents and the odds ratio and Yule’s Q values

HOW OFTEN
TYPE OF SPENDING BEHAVIOR

STUDENTS
Definitely not Not consistent Strictly

RECIEVED
following the
in following
following the
TOTAL
THEIR
set budget
the set budget set budget
ALLOWANCE
Daily and
60 55 11
126
Weekly
Monthly,
30 36 8
74
Quarterly and
not regular
Total 90 91 19
200
Odds ratio (Yule’s Q*)



Daily and
1.46 .19 1.11 .05 -

weekly
Monthly,
-
-
-

Quarterly and
Not Regular
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association



0.8 ≤|Q|<1.0 =very strong association

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following the set budget as compared to students receiving allowance monthly,
quarterly and not regular. The Yule’s Q value 0.19 implies a very weak association
between students receiving allowance daily and weekly and definitely not
following the set budget. The value 1.11 odds ratio for students who receives their
allowance daily and weekly means that their tendency to be not consistent on
following the set budget is almost the same to the tendency to be definitely
following the set budget . The Yule’s Q value 0.05 indicates a very weak
association of students who are not consistent in following the set budget.

Relationship Between Type of Spending
Behavior and Monthly Allowance

Table 12 shows the distributions of the type of spending behavior and
monthly allowance of the respondents. There is a very weak association between
the monthly income below 2,000 and definitely not following the set budget. The
odds ratio .94 means that those respondents with 2,000 and below monthly
allowance have .94 more times chance to be in the definitely not following the set
budget rather than strictly following the set budget compared to 2,0001 and above
monthly allowance. Very weak association exists between 2,000 and below
allowance with average type of spending behavior.






Multinomial Logit Analysis on Spending Behavior
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Table 12. Monthly allowance and spending behavior of the respondents and
the odds ratio and Yule’s Q values

MONTHLY
TYPE OF SPENDING BEHAVIOR

ALLOWANCE Definitely not Not consistent Strictly

following the
in following
following the
TOTAL
set budget
the set budget set budget
3000 and
75 77 16
168
below
3001 and above 15
14
3
32
Total 90 91 19
200
Odds ratio (Yule’s Q*)



3000 and above .94 -.03 1.03 .01 -

3001 and above -
-
-

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


Relationship Between Type of Spending
Behavior and Food Expense

Table 13 shows the cross tabulation of the type of spending behavior and
food expenses of the respondents. The table reveals that there is a weak association
between their food expense and their type of spending behavior. The odds ratio
1.73 and 1.25 which is greater than 1 implies a strong relationship, that shows an
association between food expense below 1,500 in the definitely not following the
set budget and with not consistent on following the set budget.





Multinomial Logit Analysis on Spending Behavior
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Table 13.
Food expense and spending behavior of the respondents and the
odds ratio and Yule’s Q values

FOOD
TYPE OF SPENDING BEHAVIOR

EXPENSE
Definitely not Not consistent Strictly

following the on following following the
TOTAL
set budget
the set budget set budget
1,500 and
83 86 18
187
below
1,501 and
7 5 1
13
above
Total 90
91 19
200
Odds ratio (Yule’s Q*)



1,500 and
1.73 .27 1.25 .11 -

below
1,501 and
-
-
-

above
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association


Relationship Between Type of Spending
Behavior and Rent Expense

Table 14 shows that is a weak association on the rent expense and type of
spending behavior of the students. The odds ratio of definitely not following the set
budget and not consistent in following the set budget as students rent expense rage
from 3,500 and below is almost the same (.70 and .71).



Multinomial Logit Analysis on Spending Behavior
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Table 14.
Rent expense and spending behavior of the respondents and the odds
ratio and Yule’s Q values

RENT
TYPE OF SPENDING BEHAVIOR

EXPENSE
Definitely not Not consistent Strictly

following the
in following
following the
TOTAL
set budget
the set budget set budget
3,500 and
77 78 17
172
below
3501 and
13 13 2
28
above
90
91
19
200
Odds ratio (Yule’s Q*)



3,500 and
.70 -.18 .71 -.17 -

below
3501 and
-
-
-

above
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association

Relationship Between Type of Spending
Behavior and Transportation Expense


Table15 shows the cross-tabulation of the type of spending and
transportation expense of the respondents. The table reveals that there is a weak
association between students’ transportation expense and the type of spending
habit. The odds ratio .70 and .96 which is close to 1.0 implies statistical
independence, that is, there is no association between transportation expense and
type of spending behavior.

Multinomial Logit Analysis on Spending Behavior
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Table 15.
Transportation expense and spending behavior of the respondents
and the odds ratio and Yule’s Q values

TRANSPORTATION
TYPE OF SPENDING BEHAVIOR

EXPENSE
Definitely
Not
Strictly

not
consistent in following

following
following
the set

the set
the set
budget
TOTAL
budget
budget
1,000 and below
83
86
18
187
1,001 and above
7
5
1
13
Total 90
91
19
200
Odds ratio (Yule’s Q*)



1,000 and below
.70 -.18 .96 -.02 -

1,000 and above
-
-
-

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association

Relationship Between Type of Spending
Behavior and Personal Expense


The Yule’s Q value shows that there is negative moderate association
between type of spending behavior and personal expense. A respondent who are
spending 1,500 and below has an odds ratio of .40 shows that there is a negative
association in definitely following and not consistent in following the set budget.









Multinomial Logit Analysis on Spending Behavior
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39

Table 16.
Personal expense and spending behavior of the respondents and the
odds ratio and Yule’s Q values

PERSONAL
TYPE OF SPENDING BEHAVIOR

EXPENSE
Definitely not Not consistent Strictly

following the
in following
following the
TOTAL
set budget
the set budget set budget
1,500 and
79 80 18
177
below
1,501 and
11 11 1
23
above
Total 90
91 19
200
Odds ratio (Yule’s Q*)



1,500 and
.40 -.43 .40 -.43 -

below
1,501 and
-
-
-

above
Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association

Relationship Between Type of Spending
Behavior and Personal Course of the Respondents


As shown in table 17, the odds of group I (BSE, BEE, BLIS and BSHE) in
definitely not and not consistent in consistent in following the set budget I 1.71 and
2.02 times greater, respectively than strictly following it over group III
(BSAS,BSIT,BSES,BSND, BSA and BSET). The association between being in group I
and group II (BSF,BSN,DVM,BSAEng and BSDC) in definitely not following the set
budget is weak and negative weak (0.26 and -0.20). The odds ratio 1.00 implies
that the tendency of group II in not consistent in following the set budget is the
Multinomial Logit Analysis on Spending Behavior
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40

same to strictly following the set budget. Not consistent in following the set budget
has no relationship on students in group II while students in group I has a weak
association.

Table 17.
Course and Spending Behavior of the Respondents and the Odds
Ratio and Yule’s Q values


COURSE
TYPE OF SPENDING BEHAVIOR

Definitely not Not consistent Strictly

following the
in following
following the
TOTAL
set budget
the
set budget
Group I
25
25
3
53
Group II
26
33
8
67
Group III
39
33
8
80
Total 90
91 19
200
Odds ratio (Yule’s Q*)



Group I
1.71 .26 2.02 .34 -

Group II
.67 -.20 1.00 0
-

Group III
-
-
-

Legend: * Yule’s Q values are bold

Interpretation: 0<|Q| < 0.2 =very weak association
0.2
≤|Q|< 0.4 =weak association
0.4
≤|Q|<0.6 =moderate association
0.6
≤|Q|<0.8 =strong association
0.8
≤|Q|<1.0 =very strong association















Multinomial Logit Analysis on Spending Behavior
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41

SUMMARY, CONCLUSION AND RECOMMENDATION


Summary


The objective of the study was to determine the association of the type of
spending behavior to socio-demographic, socio-economic and expenditures of
Benguet State University students.

Number of households’ member of the family shows a moderate association
to the type of spending behavior of BSU students. Weak to very weak association
of the type of spending behavior was revealed in regional origin, gender, year level
and food expense. The inverse of that association was seen in the source of income.

Type of spending behavior shows a positive association on the number of
household member of the family, course, monthly income of parents/guardian,
how often students receive their allowance, food expense and educational
attainment of parents/guardian. However, it implies a negative association to
educational attainment on those in the not consistent in following the set budget.

For age group it implies a very weak association to the type of spending
behavior. Furthermore, rent expense and how often parents receive their income
revealed a weak association to the type of spending behavior. From weak to
moderate association on the source of income of parents was revealed.


Multinomial Logit Analysis on Spending Behavior
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Conclusion

Based on the findings of the study, the following conclusions were drawn.
Source of income of the respondents shows a very weak to weak association.
Variables which strongly affect the decision of students to follow the set budget are
monthly income, number of household member of the family, how often students
receive their allowance, educational attainment of parents and their food expenses.
The type of spending behavior is independent from age, regional origin and
monthly allowance.

In the results it revealed that the chances of students to strictly follow the
set budget who were highlanders, ages 19 and below, males, course under group III
(BSAS, BSIT, BSES, BSND, BSA and BSET), first years to second years, with 7
and above members of the family, source of financial support is family and parents
attained secondary or vocational were higher than those belonging to other groups.
In addition, there were higher chances to follow strictly the set budget of students
whose parents’ source of income is business, personal employment, private
employee, government employee, and receiving pension, receiving income in a
daily and weekly basis, receiving allowance in monthly, quarterly and not regular,
monthly allowance of 3,001 and above, spend 1,501 and above, 3,500 and below,
1,000 and below, 1,500 and below on food, rent, transportation and personal,
respectively.

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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43


Students of BSU are more likely to be not consistent in following the set
budget if they are lowlanders, females, with monthly allowance of 3,000 and below
and with transportation expenses of 1,001 and above.

On the other hand, students with age 20 and above, female, course under
group II (BSF, BSN, DVM, BSAEng, and BSDC) , with parents source of income is
farming and monthly allowance of 3,000 and below are seen to be definitely not
following the set budget.

Recommendation

It is recommended that further study be done on the same research with the
wider scope of expenditures, spending habit and other related to spending
behaviors. The same statistical tools/analysis or other statistics can be used to
measure association, variation and other measurements. Consideration of other
objectives is also recommended.








Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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44

LITERATURE CITED


ALVES,A.T. 1983.The lifestyle and Expenditure Pattern of The Public Elementary

School Teachers of Benguet Division.Its implication to Education. Baguio
Central University. MS Thesis (Unpub.)p.7

CRAMER,C.L. and C.W JENSEN. 1979. Agricultural Economics and
Agribusness. Montano State University, New York. Wily. Pp 201-202

JENSEN. 1979. Agricultural Economics and Agribusiness. Montano State
University, New York. Wily. Pp. 201-202

KYRK, R.1933. Economic Problem of the Family. New York ; Hoper and
Brothers. p:35

CATILLO, M.R. 1978. A Developing Countries Lesson to Self-Reliance.National
Journal,vol 75-80

GABRIEL, T.M. 1973. A Study of Personal Expenditures of College andUniversity
Students in Baguio. Saint Louis University. M.S. Thesis (Unpub.) Pp.12-13

PARAN,C.P. 1981. A comparative Study on Income and Expenditure Patterns of
Farmers, and Business Owners at Mountain State Agricultural College, La
Trinidad,Benguet.M.S. Thesis (Unpub).p.46

RIVERA, L.O.1974. Expenditure and income of Rural Families in Dayap, Laguna
Philippines. Agricultural. 39:315

VALERIO, P.C.1975. Analysia of Income and Expenditure of the City of Baguio,
M.S.
Thesis
(Unpub)Pp.
3-4.




AGO, C.N. 2001. Economic Analysis of the Spending Behavior of College
Students in Selected Schools of Baguio and La Trinidad, B.S. Thesis
(Unpub.)

PUNDO, M.O. et al. 2006. Multinominal logit analysis of household cooking fuel
choice in rural Kenya: The case of Kisumu district. Kisumu, Kenya. vol. 45
http://search.yahoo.com/search?p=studies+on+multinomial+logit+model&v
c=&fr=yfp-t-501&toggle=1&cop=mss&ei=UTF-8&fp_ip=PH

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

45

http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1955298

FRITSMA, T. et al. 2005. The Contribution of Occupational Skill Requirements to
Wages and Employment Growth
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1955298

DOMINIQUE, R. 2003. Deterministic heterogeneity in tastes and product
differentiation in the K-logit model
http://www.deed.state.mn.us/lmi/publications/mms/appende.htm

BURKEY, J. and HARRIS, T.R. 2003. Modeling a Share or Proportion with Logit
or Tobit: The Effect of Outcommuting on Retail Sales Leakages

LYMP, J et al. A Random Coefficients Multinomial Logit Model for Choice

Based Conjoint Studies

http://www.stat.auckland.ac.nz/~balemi/Choice.pdf
http://www.cramster.com/reference/wiki.aspx?wiki_name=Logit





















Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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46

APPENDIX A
Letter to the President

Benguet State University
College of Arts and Sciences
Math-Physics-Statistics Department
La Trinidad, Benguet

February 4, 2009
DR. ROGELIO D. COLTING
President
Benguet State University

SIR:
We, the undersigned fourth year students taking up Bachelor of Science in Applied
Statistics at Benguet State University are conducting a research entitled
“Multinomial Logit Analysis on Spending Behavior of Benguet State University
Students”.

In view hereof, we would like to request permission from your good office to float
questionnaires to selected College Students of Benguet State University.

Thank you very much for your favorable consideration.

Respectfully yours,

Filmer A. Bagayao
Bonifacio G. Calizar Jr.

Myla A. Palao-ay
Ronald T. Lingbaoan

Noted:

DR. MARIA AZUCENA B. LUBRICA PROF. AUREA MARIE M. SANDOVAL
Thesis adviser




CAS Dean


DR. MARIA AZUCENA B. LUBRICA DR. ROGELIO D. COLTING
MPS Chairman
President
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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47

APPENDIX B


Categories and Codes of Variables
Variable Description
Observed
Variable Code



Type of Spending
Definitely not following
1
Behavior
Not consistent on following
2

Strictly following
3



Regional Origin
Highlander
1

Lowlander
2






Age
19 yrs old and below
1

20 yrs. Old and above
2



Gender
Male
1

Female
2



Course
BSE,BEE,BLIS and BSHE
1

BSF,BSN,DVM,BSAEng and
2

BSDC


BSAS, BSIT,BSES,BSND, BSA
3

and BSET




Year level
I and II
1

III and IV
2



No. of household in the
1-6
1
family
7and above
2



Sources of financial
Family
1
support
Grand scholarship and self
2

supporting




Educational attainment of
Elementary
1
person sending you to
Secondary and vocational
2
school
College level and degree holder
3



Sources of income
Business and personal
1

employment


Pensions, private employee and
2
government employee

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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48





Farming
3



Often they received their
Daily and weekly
1
income
Monthly and quarterly
2



Average monthly income
Php 15,000 and below
1

Php 15,001 above
2



Often do you receive your
Daily and weekly
1
allowance
Monthly, quarterly, not
2

regular




allowance Monthly given
3000 and below
1

3001 and above
2



Foods expenses
1500 and below
1

Php 1501 and above
2



Rent expenses
Php 3000 and below and
1

not renting


Php 3001 and above
2



Transportation expenses
Php1000 and below
1

Php 1001 and above
2



Personal expenses
Php1500 and below
1

Php 1501 and above
2













Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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APPENDIX C
Data Set
Resp
# Y
Ethnic
Age
Gender Course
Year
NOH
SFS EA
SI
RI
MI
RA
MA
FE
RE
TE
PE
1 1 1 1 2
3
2 1 1 2 2 2 2 2 1 1 1 1 1
2 1 1 2 2
3
2 2 1 1 1 1 1 1 1 1 1 1 1
3 2 1 2 1
3
2 1 1 2 1 2 1 1 1 1 2 1 1
4 2 1 1 2
1
1 2 1 1 2 2 2 1 1 1 1 1 1
5 2 1 2 2
1
2 2 1 3 1 2 2 1 1 1 2 1 1
6 3 1 1 1
3
1 1 1 2 2 2 1 2 1 1 1 1 1
7 2 1 2 1
3
2 2 1 2 2 2 1 2 1 1 1 1 1
8 2 1 2 2
3
1 2 1 2 2 2 1 2 1 1 1 1 1
9 2 1 2 2
3
2 2 1 2 3 2 1 2 1 1 1 1 1
10 2 1 2 1
2
1 2 1 3 2 1 1 1 1 2 1 1 1
11 3 2 2 1
2
2 2 1 3 2 1 1 1 1 1 1 1 1
12 2 2 2 1
2
2 1 1 2 3 2 1 2 1 1 1 1 1
13 1 1 1 1
3
2 1 1 3 2 2 1 2 2 1 1 1 1
14 1 1 2 2
1
2 2 1 1 2 2 1 2 1 1 1 1 1
15 2 2 2 2
2
1 2 1 2 3 2 1 1 1 2 1 2 1
16 2 1 2 2
1
1 2 1 3 3 2 1 2 1 1 1 1 1
17 2 1 1 2
2
1 1 2 1 1 1 1 1 2 2 2 2 2
18 2 2 1 1
3
1 1 1 3 3 2 1 2 2 1 2 1 2
19 2 2 1 2
3
1 2 1 1 2 2 1 2 1 1 1 1 1
20 1 1 1 1
3
1 1 1 2 1 1 1 1 1 1 1 1 1
21 2 1 2 1
1
2 2 1 2 2 2 1 1 1 1 1 1 1
22 1 2 2 2
3
2 2 2 1 2 2 1 2 1 1 1 1 1
23 2 1 2 2
3
2 2 1 1 2 2 1 2 1 1 1 1 1
24 2 1 2 1
2
2 2 2 2 1 2 1 1 1 1 1 1 1
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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25 2 1 2 2
2
2 1 1 2 2 2 1 1 1 1 1 1 2
26 1 2 1 1
1
1 2 1 2 1 2 1 2 2 1 1 1 1
27 2 2 1 2
2
1 2 1 3 3 2 2 2 1 2 1 1 1
28 3 1 1 2
3
1 2 1 2 2 2 1 1 1 1 1 1 1
29 2 1 2 2
2
2 2 2 2 1 2 1 1 1 1 1 1 1
30 1 1 2 2
3
2 2 2 2 3 2 1 2 1 1 1 1 2
31 1 1 1 1
2
1 2 1 2 2 2 1 2 1 1 1 1 1
32 2 1 2 1
1
2 1 1 2 2 2 1 2 1 1 1 1 1
33 2 2 2 1
2
2 2 1 2 1 2 1 1 2 2 1 1 1
34 2 1 2 2
2
2 2 1 1 2 2 1 2 1 1 1 1 1
35 1 2 2 2
3
2 2 1 2 2 2 1 2 1 1 1 1 1
36 1 2 2 1
1
2 2 1 2 1 2 1 2 2 1 1 1 1
37 1 1 1 1
3
1 1 1 2 2 2 1 1 1 1 1 1 1
38 3 1 1 2
2
1 2 1 2 1 2 1 1 1 1 1 1 1
39 2 1 1 2
3
1 1 1 1 2 2 1 1 1 2 1 1 1
40 2 1 2 2
3
2 2 1 2 2 2 1 2 1 1 1 1 2
41 2 1 2 1
1
2 2 1 2 2 2 1 2 1 1 1 1 1
42 1 2 2 1
3
2 2 1 2 1 2 1 1 1 1 1 1 1
43 1 1 1 2
2
1 2 1 2 2 2 1 2 2 1 1 2 1
44 1 1 1 2
2
1 2 1 1 2 2 1 2 1 1 2 1 1
45 2 1 1 2
1
1 2 1 2 1 2 1 2 1 1 1 1 1
46 2 1 2 2
2
2 1 1 2 2 2 1 1 1 1 1 1 1
47 2 2 2 1
3
2 2 1 2 2 2 1 1 1 1 2 1 1
48 1 2 2 1
2
2 2 2 3 3 2 1 1 1 1 1 1 1
49 1 2 2 1
2
2 2 1 3 3 2 2 1 1 1 1 1 1
50 2 2 2 1
3
2 1 1 2 1 2 1 2 1 1 1 1 1
51 1 1 2 2
2
2 1 1 2 2 2 1 2 1 1 1 1 1
52 1 1 1 2
1
1 2 1 2 2 2 2 2 1 1 1 1 1
53 1 1 2 2
3
2 2 1 2 2 2 1 1 2 1 1 1 1
54 2 1 1 2
2
1 2 1 3 1 2 2 2 1 1 1 1 1
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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55 2 1 2 1
3
2 2 1 2 1 1 1 2 1 1 1 1 1
56 1 2 2 2
2
2 1 1 2 2 2 1 1 1 1 1 1 1
57 1 2 1 1
3
1 1 1 2 2 1 1 1 1 1 1 1 1
58 2 1 2 1
1
2 1 1 2 1 2 1 2 1 2 1 1 1
59 3 2 1 2
2
1 2 1 2 2 2 2 2 2 2 1 1 1
60 2 1 1 2
3
1 1 1 3 3 2 2 1 1 2 1 1 1
61 1 1 2 2
2
2 1 1 3 2 2 1 1 1 1 1 1 1
62 2 1 2 1
1
2 2 1 2 2 2 1 1 1 1 1 1 1
63 2 1 1 1
3
2 2 1 2 3 2 1 1 1 1 1 1 1
64 2 1 1 2
2
1 2 1 1 2 2 2 1 1 1 2 1 1
65 1 2 1 2
3
1 2 1 1 2 2 2 2 2 1 1 1 1
66 1 2 2 2
2
2 2 1 2 1 1 2 2 1 1 1 1 1
67 1 1 2 2
1
2 1 1 2 1 1 1 1 1 1 2 1 1
68 3 1 2 2
2
2 2 1 2 2 2 2 1 1 2 1 1 1
69 1 2 1 1
3
1 2 1 3 3 2 2 1 1 1 1 1 1
70 1 2 1 2
2
1 2 1 3 2 2 2 1 2 1 1 1 1
71 1 1 2 2
3
2 1 1 2 2 2 1 1 2 1 1 1 2
72 2 1 2 1
1
2 2 2 2 3 2 2 1 1 1 1 1 1
73 2 2 1 1
3
1 2 2 3 3 2 2 1 1 1 1 1 1
74 1 2 1 1
2
1 1 2 2 2 2 2 1 1 1 2 1 1
75 2 2 2 2
3
2 2 2 2 2 2 2 1 1 1 1 1 1
76 2 1 2 2
2
2 2 2 2 1 1 1 2 1 1 1 1 1
77 2 1 1 2
1
1 2 1 1 1 1 1 1 2 1 1 1 1
78 1 2 1 2
2
1 1 1 1 2 2 2 1 2 1 2 1 1
79 1 2 1 1
3
1 1 1 2 2 2 2 1 2 1 1 1 1
80 3 1 2 1
2
2 2 1 2 3 2 1 2 1 2 1 1 1
81 3 1 2 1
1
2 1 1 2 2 2 2 1 1 1 2 1 1
82 2 2 1 2
3
1 1 1 1 2 2 2 1 1 1 1 1 2
83 1 2 1 2
2
1 2 1 1 1 1 1 1 1 1 1 2 1
84 1 1 2 2
3
2 2 1 1 1 2 2 2 1 1 1 2 1
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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85 1 1 1 1
1
2 1 1 2 2 2 2 1 1 1 1 1 1
86 2 1 2 2
2
2 2 2 2 2 2 1 2 1 1 1 1 2
87 2 1 1 2
3
1 1 2 3 3 2 1 1 1 1 1 1 1
88 2 1 2 2
2
2 2 1 3 3 2 2 1 1 1 1 1 1
89 1 1 2 1
1
2 2 1 2 2 2 1 1 1 2 1 1 2
90 1 2 2 1
3
2 1 1 1 2 1 1 2 1 2 1 1 2
91 2 1 1 2
2
1 1 2 1 2 1 1 1 1 1 1 1 2
92 2 2 1 2
1
2 2 1 1 2 2 1 1 1 1 2 1 1
93 3 2 2 2
2
2 2 1 1 2 2 1 1 2 1 1 1 1
94 2 2 1 2
3
2 2 2 2 2 1 2 1 1 1 1 1 1
95 2 1 1 1
1
1 2 1 2 2 1 1 2 1 1 1 1 1
96 1 1 2 1
3
2 2 1 2 2 2 1 1 1 1 2 1 1
97 1 1 2 1
1
1 1 1 2 2 2 1 1 1 1 1 2 1
98 1 2 1 2
1
1 1 1 3 3 1 1 1 1 1 2 2 1
99 1 1 1 2
2
1 1 1 3 1 2 1 1 1 1 1 1 1
100 1 1 1 2
1
1 2 2 2 1 1 1 2 1 1 1 1 1
101 2 1 1 2
1
1 2 2 2 2 1 1 1 1 1 1 1 1
102 1 1 1 1
3
1 2 1 3 3 2 2 1 1 2 1 1 1
103 1 2 2 1
2
2 1 1 3 3 2 2 1 2 1 2 1 2
104 2 2 2 1
1
1 1 1 3 3 2 1 1 1 1 1 2 1
105 2 1 1 2
1
1 1 1 3 1 2 1 2 1 1 1 1 1
106 2 1 1 2
1
1 2 2 2 1 1 1 2 1 1 1 1 1
107 2 1 1 1
2
1 2 2 2 2 1 1 1 2 1 1 1 1
108 2 2 2 2
1
2 1 1 1 2 1 2 1 1 2 1 1 2
109 2 1 1 1
2
1 1 1 1 2 1 1 1 2 1 2 1 1
110 1 2 2 2
3
2 2 1 1 2 1 2 1 2 1 2 1 1
111 2 2 2 2
1
2 2 1 2 2 2 2 2 1 1 1 1 1
112 1 1 2 2
1
2 2 1 2 2 2 2 1 1 1 1 1 1
113 1 1 2 2
1
2 1 1 2 3 2 2 1 1 1 1 1 2
114 2 2 2 1
2
2 1 2 2 2 2 1 1 1 2 2 1 1
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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115 2 1 2 1
1
2 1 1 2 2 2 2 2 1 1 1 2 1
116 1 1 1 2
1
1 1 1 2 1 1 1 1 1 1 1 1 1
117 2 2 1 2
1
1 2 2 2 1 2 2 1 1 1 1 1 1
118 1 2 1 2
1
1 1 2 2 2 2 1 1 1 1 1 1 1
119 2 1 2 2
1
2 1 1 2 2 2 1 1 2 2 2 1 1
120 1 1 2 2
3
2 2 1 2 2 2 1 2 1 1 1 1 1
121 1 1 1 2
3
1 1 1 2 1 2 1 1 1 1 1 1 1
122 1 1 2 2
1
2 2 1 1 2 2 2 2 1 1 1 1 1
123 2 1 1 1
3
1 2 1 2 2 2 2 1 1 1 1 1 1
124 2 2 1 1
2
1 1 2 2 1 1 1 2 2 1 2 1 1
125 2 1 1 1
2
1 1 2 2 2 1 1 1 1 2 2 1 1
126 2 1 1 1
1
1 2 2 2 1 2 1 1 1 1 1 1 1
127 2 2 1 2
3
1 1 1 2 2 2 1 2 2 1 1 1 1
128 1 2 2 2
2
2 2 1 1 2 2 2 1 1 1 1 1 1
129 3 2 2 2
3
2 2 2 2 2 2 2 1 1 1 1 1 1
130 1 2 2 1
2
2 2 2 3 3 2 1 1 1 1 1 1 1
131 3 1 2 1
1
2 2 2 2 1 1 1 1 2 1 1 1 1
132 2 1 2 2
2
2 1 1 3 3 2 1 1 1 1 2 1 2
133 3 1 1 2
2
1 2 1 2 2 2 2 2 1 1 1 2 1
134 3 2 1 2
3
1 1 1 3 3 2 1 1 1 1 1 1 1
135 2 1 1 1
2
1 1 2 2 2 2 2 1 1 1 1 1 1
136 1 1 1 1
1
1 1 1 2 1 1 1 1 1 2 1 2 1
137 2 2 1 2
2
1 2 2 2 2 2 2 2 1 1 1 1 1
138 1 2 2 2
3
2 1 2 2 2 2 1 1 1 1 1 1 1
139 2 2 1 2
2
1 2 1 2 1 1 1 2 1 1 1 1 1
140 1 1 1 2
1
1 2 1 2 3 2 2 1 1 1 1 1 1
141 2 1 1 1
3
1 2 1 3 3 2 2 1 1 1 1 1 1
142 1 1 2 1
2
1 2 2 2 1 1 2 2 1 1 1 1 1
143 1 2 1 2
1
1 1 1 1 1 2 1 1 1 1 2 1 2
144 1 1 2 2
1
1 2 1 2 2 2 1 2 1 1 1 1 2
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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145 1 2 1 1
3
1 2 1 2 2 2 1 1 2 1 1 1 1
146 2 1 1 1
1
1 2 1 2 1 1 2 2 2 1 1 1 1
147 2 1 1 1
1
1 1 2 3 3 2 2 1 2 2 1 1 1
148 2 2 1 2
3
1 1 1 3 3 2 1 1 1 1 1 1 1
149 1 1 1 2
1
1 2 2 2 2 2 2 1 1 1 1 1 2
150 1 1 2 2
1
2 2 1 3 1 1 2 1 1 1 1 1 1
151 3 1 2 1
1
2 2 1 1 1 1 1 1 1 1 1 1 1
152 1 2 2 1
1
2 2 1 1 1 1 1 1 2 1 1 1 1
153 1 2 1 2
1
2 1 2 1 2 2 1 1 2 1 1 1 1
154 1 1 1 2
3
1 1 1 2 2 2 1 1 1 1 2 1 2
155 1 1 1 2
3
2 2 1 2 2 2 1 2 1 1 1 1 1
156 3 1 2 2
3
1 2 1 2 1 1 1 2 1 1 1 1 1
157 2 2 2 1
1
1 1 2 2 2 2 1 1 2 1 1 1 1
158 2 2 1 2
3
1 2 1 1 2 2 2 1 1 2 1 1 1
159 1 1 1 2
3
1 2 1 1 2 2 2 1 1 2 1 1 1
160 1 1 1 1
3
1 1 2 2 2 2 2 1 1 2 1 1 1
161 2 2 1 1
1
1 1 1 2 2 1 2 2 1 1 1 1 1
162 2 2 2 2
3
2 1 1 2 2 2 2 2 1 1 1 1 1
163 1 2 1 2
1
1 2 1 2 2 2 2 2 1 2 2 1 1
164 1 1 1 2
3
1 2 1 2 1 1 2 2 1 2 2 1 1
165 3 1 1 2
2
1 2 1 2 2 2 2 2 1 1 1 1 1
166 1 2 2 2
1
2 2 1 3 2 2 1 2 1 1 1 1 2
167 2 2 2 1
3
1 1 2 2 1 2 2 1 1 1 1 1 1
168 2 2 2 1
2
2 1 1 2 2 2 2 1 1 2 1 1 1
169 2 1 2 1
3
2 1 1 2 1 2 1 1 1 1 1 1 2
170 1 1 2 1
3
2 2 1 2 2 2 2 1 1 1 1 1 1
171 1 1 2 2
1
2 2 2 1 2 2 1 1 1 1 1 1 1
172 1 2 2 2
3
2 2 1 1 1 2 2 1 1 1 1 1 1
173 1 1 2 2
1
2 1 1 1 2 2 2 1 1 1 1 1 1
174 1 1 1 2
3
2 1 1 2 2 2 1 1 1 1 1 1 1
Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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175 2 2 1 2
3
1 1 1 2 1 2 2 1 1 1 1 2 1
176 2 2 2 1
2
2 1 2 1 2 2 2 1 1 2 1 1 1
177 2 2 2 2
2
2 1 1 1 2 2 2 2 1 1 1 1 1
178 2 2 2 2
3
2 2 1 1 2 2 2 1 2 1 1 1 1
179 2 1 2 1
2
2 2 1 1 2 2 2 1 2 1 1 1 1
180 1 1 1 2
2
1 2 1 2 1 2 1 1 1 2 1 1 1
181 1 1 1 2
3
1 2 1 2 2 2 2 1 1 2 1 1 1
182 1 1 1 1
2
1 2 2 2 2 2 1 1 1 1 1 1 1
183 1 2 2 2
3
1 2 1 2 1 2 1 1 1 1 1 2 1
184 2 2 1 2
2
1 2 1 3 3 2 2 1 1 1 1 1 1
185 3 2 1 1
3
1 2 1 2 2 2 2 1 1 2 2 1 2
186 1 1 2 2
2
2 1 1 2 2 2 2 1 1 1 2 1 1
187 3 1 1 1
3
1 2 1 1 1 1 2 2 1 1 1 1 1
188 1 2 1 2
2
1 1 2 1 1 1 2 1 1 1 1 1 1
189 1 2 2 2
3
2 1 1 1 1 2 2 1 1 2 1 1 1
190 1 2 2 1
2
2 2 1 2 3 2 2 1 1 2 1 1 1
191 2 1 1 1
3
1 2 1 2 1 1 2 1 1 1 1 1 2
192 3 2 1 1
3
1 1 1 2 1 1 2 2 1 1 1 1 1
193 1 1 1 2
3
1 1 2 2 2 2 1 2 1 1 1 1 1
194 2 2 1 2
2
1 1 1 1 2 2 1 2 1 1 1 1 1
195 1 1 2 2
2
2 2 1 1 2 2 1 2 1 1 1 1 1
196 1 2 2 2
2
2 2 2 2 2 2 2 1 1 1 1 1 1
197 1 1 2 1
3
2 2 1 2 2 2 2 2 1 1 1 1 1
198 2 2 2 2
2
2 1 1 2 2 2 1 2 1 1 1 1 1
199 2 1 2 2
3
2 1 2 2 2 2 1 2 1 1 1 1 1
200 1 2 1 2
3
2 1 1 1 2 2 2 2 1 1 1 1 1




Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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Legend:
Y (Dependent variables) = Type of Spending Behaviour Resp.#=Respondents Number



Ethnic= Ethnic of Origin




RE = Rent Expenses Monthly
Year= Year Level




NOH= Number of Household in the family
SFS = Sources of Financial support
EA = Educational attainment of parents/ guardians
SI = Sources of Income of parents/guardians

RI= How often do they receive income
MI= Average Monthly Income



RA = how often do you receive allowance
MA = Monthly allowance


FE= Food Expenses Monthly

TE = Transportation Expenses Monthly PE = Personal Expenses Monthly



















Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

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APPENDIX D
Result of Multinomial Logit Model

Nominal Regression
Case Processing Summary
N
Y
1
90
2
91
3
19
REGION
1
119
2
81
Valid
200
Missing
0
Total
200

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.692
.411
16.916
1
.000
[REGION=1]
-.225
.521
.187
1
.666
.798
.287
2.217
[REGION=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.638
.413
15.716
1
.000
[REGION=1]
-.115
.522
.049
1
.825
.891
.321
2.478
[REGION=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
AGE
1
97
2
103
Valid
200
Missing
0
Total
200

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.631
.364
20.034
1
.000
[AGE=1]
-.150
.506
.088
1
.767
.861
.320
2.319
[AGE=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.674
.363
21.238
1
.000
[AGE=1]
-.215
.505
.182
1
.670
.806
.300
2.170
[AGE=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
GENDER
1
79
2
121
Valid
200
Missing
0
Total
200

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.792
.342
27.518
1
.000
[GENDER=1]
-.588
.511
1.323
1
.250
.556
.204
1.512
[GENDER=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.629
.346
22.193
1
.000
[GENDER=1]
-.138
.506
.074
1
.786
.871
.323
2.348
[GENDER=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
YEAR
1
99
2
101
Valid
200
Missing
0
Total
200

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.812
.381
22.590
1
.000
[YEAR=1]
-.497
.511
.946
1
.331
.609
.224
1.655
[YEAR=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.705
.384
19.673
1
.000
[YEAR=1]
-.252
.510
.245
1
.620
.777
.286
2.110
[YEAR=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
NOH
1
80
2
120
Valid
200
Missing
0
Total
200

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.281
.292
19.261
1
.000
[NOH=1]
.916
.602
2.313
1
.128
2.500
.768
8.143
[NOH=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.224
.294
17.359
1
.000
[NOH=1]
1.079
.601
3.221
1
.073
2.941
.906
9.553
[NOH=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
SFS
1
155
2
45
Valid
200
Missing
0
Total
200

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
2.140
.748
8.196
1
.004
[SFS=1]
-.683
.795
.738
1
.390
.505
.106
2.398
[SFS=2]
0a
.
.
0
.
.
.
.
2
Intercept
2.565
.734
12.218
1
.000
[SFS=1]
-1.224
.783
2.444
1
.118
.294
6.342E-02
1.364
[SFS=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
EA
1
45
2
123
3
32
Valid
200
Missing
0
Total
200

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.872
.760
6.073
1
.014
[EA=1]
.165
.977
.029
1
.866
1.179
.174
7.998
[EA=2]
-.522
.817
.408
1
.523
.593
.120
2.941
[EA=3]
0a
.
.
0
.
.
.
.
2
Intercept
2.140
.748
8.196
1
.004
[EA=1]
-.294
.972
.092
1
.762
.745
.111
5.007
[EA=2]
-.772
.805
.919
1
.338
.462
9.536E-02
2.240
[EA=3]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
SI
1
54
2
115
3
31
Valid
200
Missing
0
Total
200

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.705
.769
4.918
1
.027
[SI=1]
-.318
.894
.127
1
.722
.727
.126
4.194
[SI=2]
-9.53E-02
.837
.013
1
.909
.909
.176
4.686
[SI=3]
0a
.
.
0
.
.
.
.
2
Intercept
2.197
.745
8.690
1
.003
[SI=1]
-.811
.874
.861
1
.353
.444
8.014E-02
2.465
[SI=2]
-.703
.817
.742
1
.389
.495
9.987E-02
2.453
[SI=3]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
RI
1
42
2
158
Valid
200
Missing
0
Total
200


Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.726
.301
32.855
1
.000
[RI=1]
-.684
.562
1.480
1
.224
.505
.168
1.519
[RI=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.712
.301
32.264
1
.000
[RI=1]
-.559
.557
1.008
1
.315
.572
.192
1.703
[RI=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
MI
1
119
2
81
Valid
200
Missing
0
Total
200


Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.440
.371
15.096
1
.000
[MI=1]
.208
.507
.169
1
.681
1.232
.456
3.324
[MI=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.329
.375
12.572
1
.000
[MI=1]
.411
.508
.656
1
.418
1.509
.557
4.084
[MI=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
RA
1
126
2
74
Valid
200
Missing
0
Total
200


Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.322
.398
11.034
1
.001
[RA=1]
.375
.516
.528
1
.467
1.455
.529
3.996
[RA=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.504
.391
14.807
1
.000
[RA=1]
.105
.512
.042
1
.837
1.111
.408
3.029
[RA=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
MA
1
168
2
32
Valid
200
Missing
0
Total
200



Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.609
.632
6.476
1
.011
[MA=1]
-6.45E-02
.690
.009
1
.925
.938
.243
3.624
[MA=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.540
.636
5.863
1
.015
[MA=1]
3.077E-02
.693
.002
1
.965
1.031
.265
4.011
[MA=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Case Processing Summary
N
Y
1
90
2
91
3
19
FE
1
168
2
32
Valid
200
Missing
0
Total
200

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.099
.577
3.621
1
.057
[FE=1]
.550
.643
.733
1
.392
1.733
.492
6.106
[FE=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.386
.559
6.150
1
.013
[FE=1]
.223
.626
.127
1
.722
1.250
.366
4.268
[FE=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.
Case Processing Summary
N
Y
1
90
2
91
3
19
RE
1
172
2
28
Valid
200
Missing
0
Total
200


Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.872
.760
6.073
1
.014
[RE=1]
-.361
.805
.201
1
.654
.697
.144
3.378
[RE=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.872
.760
6.073
1
.014
[RE=1]
-.348
.805
.187
1
.665
.706
.146
3.422
[RE=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.


Case Processing Summary
N
Y
1
90
2
91
3
19
TE
1
187
2
13
Valid
200
Missing
0
Total
200


Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
1.946
1.069
3.313
1
.069
[TE=1]
-.417
1.100
.144
1
.704
.659
7.624E-02
5.691
[TE=2]
0a
.
.
0
.
.
.
.
2
Intercept
1.609
1.095
2.159
1
.142
[TE=1]
-4.55E-02
1.126
.002
1
.968
.956
.105
8.679
[TE=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.



Parameter Estimates
95% Confidence Interval for
Exp(B)
Y
B
Std. Error
Wald
df
Sig.
Exp(B)
Lower Bound
Upper Bound
1
Intercept
2.398
1.044
5.271
1
.022
[PE=1]
-.919
1.077
.728
1
.393
.399
4.837E-02
3.291
[PE=2]
0a
.
.
0
.
.
.
.
2
Intercept
2.398
1.044
5.271
1
.022
[PE=1]
-.906
1.077
.709
1
.400
.404
4.898E-02
3.333
[PE=2]
0a
.
.
0
.
.
.
.
a. This parameter is set to zero because it is redundant.

Multinomial Logit Analysis on Spending Behavior
of Benguet State University Students / Filmer A. Bagayao; et al. 2009

---

Document Outline

• Multinomial Logit Analysis on Spending Behavior of Benguet State University Students
  • BIBLIOGRAPHY
  • ABSTRACT
  • TABLE OF CONTENTS
  • INTRODUCTION
  • REVIEW OF RELATED LITERATURE
  • THEORETICAL FRAMEWORK
  • METHODOLOGY
  • RESULTS AND DISCUSSION
  • SUMMARY, CONCLUSION AND RECOMMENDATION
  • LITERATURE CITED
  • APPENDIX

---