This is an abstract in English about my bachelor’s thesis that it is found on http://pt.slideshare.net/Hermannelcio/monografia-hermann-gruber

1. Analysis of Demand, Cost and Income of a Private Education
Institution - A Study Case
Hermann Elcio Annies Gruber
UEL State University Londrina, Brazil
Final Paper of Economic Science Degree at Social Studies applied Center CESA, 2008 .1
Purpose
This is an abstract about my bachelor’s thesis.
Introduction
Education in Brazil has been suffered a continual decrease of quality. It has been
measured by PISA’s exams that it is managed by UNESCO. Moreover, there was a new regulatory
framework in 1997 under Fernando Henrique Cardoso government that changed the Law of
Directive and Bases of National Education. It has changed the relations between demand and
supply for education. Additionally, the advent and spreading of the internet and Microsoft
programmes have altered considerably the relationship not only between teachers and students but
also teaching methods. Another fundamental modification was the upbringing in which, this decade
all students have been born under the Law of Child and Teenage Protection (1979). This essay aim
shed light the factors culprit of students demand for private schools and apply this knowledge to an
education institute.
Available on <http://pt.slideshare.net/Hermannelcio/monografia-hermann-gruber> accessed on: 22/03/2016.1

2. 2
Economic theory applied to Education
Considering the economic premises of consumer theory it is had the demand for
private education is a function mainly of price and income. Especially there is a peculiar
characteristic of their price function such that it most depends on simple consumers opinion. Thus,
the education in some institution could be considered merely as a luxury item.
As all families spend their income making a basket of goods the consumers will
choose between education and others goods. In this case, it is possible to observe the consumer's
choice for whatever they consider a priority, which means 'we part of the principle that the
preferences are complete in meaning that the consumer can put in order all the goods
basket' (BROWNING, 2004) and services.
On the other hand, in Brazil, as in many countries, in the case of basic goods such
as health, education and transport are determined by dissatisfaction which makes us ask private
education is a good or a bad opposed? Brazilian public schools have been attacked by violence,
encouraging the consumption of drugs, lack of investment, strikes and so on. Unfortunately, the
existence of these bad things in public school turns out to be benefits for Private Education
Institutions (PEI), as a result, it stimulates families to enroll their children in these schools.
Nevertheless, one should pay more attention to transport the issue of
complementarity to private education. In 1997, we moved from a level of US$ 11.00 a barrel of oil
to US$ 140.00 in 2008. This obviously reduced the demand radius of each PEI in Londrina and in
a sense has turned into others PEI a regional monopoly in the city.
Data Base and Methodology
The data base used in this study is from: School Census CE (2003), the Census of
the Magisterium Professionals CPM (2003), the National Examination of high school ENEM (2003)
- conducted by the National Institute of Educational Studies Teixeira (INEP) - the IXAM secretariat
data and college treasury data (1998-2008), and Londrina city Profile data PML (2007), which is
from 2006 but it has information since the creation of the county and is organized by the Londrina
council.
To do this analysis it is used a classification for the income level among students'
families (NR's) enrolled in private schools. As the ENEM (2003) have made a classification of the
NR's, which we have adopted as a database; for synthesis purposes and practicality it is used the
information relating to NR's already adopted by ENEM in what it is called NR:
(A): families having its income up to 1 minimum wage that is up to R$ 240.00
which was the minimum wage in that year;
(B): 1 to 2 minimum wages which are an income from R$ 240.01 to R$ 480.00;
(C): from 2 to 5 minimum wages comprising a familiar income between R$ 480.01
and R$ 1,200.00;
(D): from 5 to 10 minimum wages comprising a familiar income between R$
1,200.01 and R$ 2,400.00;
(E): from 10 to 30 minimum wages comprising a familiar income between R$
2,400.01 and R$ 7,200.00;
(F): 30 to 50 minimum wages comprising a familiar income between R$ 7,200.00
to R$ 12,000.00;
(G): more than 50 minimum wages that are a familiar income greater than R$
12,000.00; and
(N): those who would not answer or left blank.

3. 3
In this estimation uses a stochastic model in which "describes the relationship
between two or more random variables having defined probabilities and not necessarily
equal" (SANDRONI, 2005). Then using cut data which is collected at one point in time, in this case
it was collected in 2003. To make this analysis is necessary to compare the physical structure of each
college analyzed in order to register information regarding consumer preferences in that point of
time.
So the equation that determines the feature of school is more attractive for
families is this:
! (1)
Wherein:
Qd = the number of students enrolled in institutions that offer high school level
(EM) in Londrina;
Dummyes variables (D): 1 for presence and 0 for absence of the resource, as
follows:
D1 = 1 if there are all levels of education in school,
D2 = 1 if there is a video library,
D3 = 1 if there is lunch hall,
D4 = 1 if there is a computer lab,
D5 = 1 if there is a open air court,
D6 = 1 if there is a indoor court,
D7 = 1 if there is a pool,
D8 = 1 if there are lessons in the science lab,
MT = is the students ratio per class;
E = the students' average score in ENEM exam;
AR = the amount of air conditioners in school;
n = NR which is the income level
i = 1 to 13.
The expectation for D2, D3, D4, D5, D6, D7 and D8 variables is they have a
direct relationship to the demand. They are binary variables that are expected their coefficients
inform the plus demanded if these features are being in the institution which means they have the
value of 1.
It is expected that the MT variable has an indirect relationship on the demand
that is the fewer students per class the greater the number of students enrolled.
The method of Ordinary Least Squares (OLS) depends on the following
hypotheses/assumptions: the regression model is linear in the parameters, the values of the
explanatory variables are non-stochastic which means that the values assumed by the regressors are
fixed in repeated samples; the average value of the error term is zero, the sample has
homoscedasticity that means the variance of each regressor value is equal, there is no
autocorrelation among the disturbances, the model is correctly specified and there is no
multicollinearity.
There is another way to estimate the demand for private education, which is using
IXAM school data, using time series form, which is available.
In this analysis also it was used up the log-linear model. The variable parameters
provided by this model are already the result of elasticity, which allows saving time and analysis and
to be objective. The results of these regressions easily take in the forecasts.
iiiiiiiiiiini uAREMTDDDDDDDQd +++++++++++= 3217654321 )8654321( γγγδδδδδδδα

4. 4
IXAM data are in time series and comprising the average value of monthly fees by
NR - adopted the same NR's classification ENEM, the number of students enrolled by NR, the
price of the supplement good which is gas, the high school quantity demanded in private schools of
Londrina, the real GDP per capita of Londrina city and general enrollment in high school in
Londrina.
Table 1 - Trend of the regression coefficients and elasticities of statistically significant factors for
NR.
Elasticity of the estimated parameters of the demand analysis made by cutting data
A B C D E F G N
Factors Tobit Tobit OLS OLS OLS Tobit * Tobit OLS
Const. < 0 < 0 - -152.126 -300.734 -49.094 < 0 -
D1 > 0 > 0 40.848 - - - > 0 -
D2 > 0 < 0 - - - -34.427 > 0 -60.158
D3 < 0 < 0 - 77.138 270.545 60.001 > 0 34.867
D4 > 0 > 0 55.46 166.173 447.384 42.097 > 0 113.155
D5 < 0 > 0 -35.402 -97.558 - 23.901 > 0 34.969
D6 > 0 > 0 - -53.811 - 53.292 > 0 85.29
D7 > 0 < 0 - - - - < 0 -
D8 < 0 > 0 40.242 - 167.18 59.713 > 0 100.104
MT > 0 < 0 - 1.214 - -18.167 < 0 -11.766
Ea < 0 < 0 - - - - > 0 -4.565
AR > 0 > 0 0.961 0.495 2.911 6.472 > 0 4.501
Elasticities of the estimated parameters of the demand analysis in time series -
Cochrane-Orcutt
Const. 138.35 52.492 26.51 90.009 24.424 -33.986 -2.653 -34.947
Pn -0.27 -0.389 - -1.766 -0.541 4.555 0.746 -
Gas - - - - - - 0.401 -
QdGL - 4.691 0.708 - 3.078 - - -3.495
PIBPCr -1.52 3.468 - - 3.065 -3.89 0.454 -2.821
MEMEP -12.33 -10.929 -3.047 -7.707 -6.41 32.526 1.075 8.849
SOURCE: Made by Author
* The elasticities of NR F were obtained eliminating the collinear variables and reestimating through an appropriate
programme.

5. 5
The equation that determines the demand for enrollment in private schools by
restricting families is as follows:
! (2)
Wherein: Qdi = is the quantity demanded of students by NR;
Pi = is the average price negotiated by NR;
Gasi = is the monthly gasoline;
QdGLi = is the number of students enrolled in high school at PEI's;
PIBPCri = is real city GDP per capita;
MEMEPi = total enrollment in high school in the public system;
ui = is the error term;
! = is the parameters of the variables analyzed.
Results and discuss
Equation 1 was applied to 7 different social classes and who did not declare
income (N).
As some dependent variables in the data classes have values equal to zero, we used
the censored regression model Tobit for classes A, B, F and G. For the other was applied to ordinary
least squares (OLS) being C, D , E and N.
The estimations made using Tobit are presented in table 1. It has not been
possible to obtain the marginal effects of parameter estimates from the NR's A and B but their
coefficients and signs inform the direction that the demand takes arising from decisions made.
It can be seen that there are significant estimated parameters of NR's A, B and C
of D1 variable (existence of all education levels). Whereas, the existence of all levels of education
make a little or no importance for classes NR D and higher. Yet considering D1 = 1 has been a
change in the demand intercept by NR C in 40 students, which is a high factor considering only this
NR.
Since the variable D2 (existence of library) was negligible for the (hypothetical)
middle classes NR C, D and E. And negative parameter estimate for the remainder of the
regressions except NR A. It is due to the creativity of teachers find material that fits their classes.
Moreover, it shows that students do not depend on the existence of a video library, in the case of
high school. One can not disregard entirely this feature. But these data can be caused by heavy
investment in large facilities aiming to prepare for the college exams only what it would make the
job of a librarian to form a library probably an unnecessary cost when it comes to high school.
The existence of lunch hall (D3) makes a big difference between the classes D, E,
F and N, due to ease a lunch hall provides for students and its parents as a result, it becomes
unnecessary traffic to homes which is an expenditure of 2 hours of the day in which parents from
this NR probably work more than 40 hours per week. This makes it more convenient and
economical for families students eating at school taking into account that many activities have in
counter-turn. The existence of this feature increases the intercept of the demand curve in 77, 270,
60 and 34 for NR D, E, F and N respectively.
The parameter D3 = 1 reduces the value of the constant shifting the demand
curve downward for NR A and B. A characteristic of the families of these NR as probably the PEI
will charge in larger amounts in fees for this space in their PEI, and also for these families to feed off
their home is often considered superfluous.
iiiiiii uMEMEPPIBPCrQdGLGasPQd ++++++= 54321 βββββ
α
β

6. 6
The computer lab existence (D4) has positive effects on all NR. In that D4 = 1
raises the intercept at 55, 166, 447, 42, 34 and 113 for the N's C, D, E, F, G and C respectively. It is
natural this family concerning due to recent society characteristic where almost everything involves
technology. In other words, knowing technology has become as important as Languages or
mathematics.
It should be noted that only large schools and new schools have open air court
(D5). Since so in comparison of the NR's it could consider that this factor makes no difference due
the significant estimates and negative parameters for NR's C and D and positive for NR's F and G
and N. thus, here it is a matter of logic: the NR's F, G and N are in large schools and, of course,
these schools have great sports structure having several blocks.
However as no one knows a number of blocks it might be checked the influence
of the size of the school in this variable. Since the N's C and D are present in all schools having
parameters negative estimated which it reduces the intercept of pupils in a number of 35 and 97
respectively. One can understand that it is important to have a usable sports structure independent
from the weather to improve the demand for students where it would be natural a minimum of 3
indoor sports modules to consider courts as demanding factor of students with high NR's.
What happens to the variable D6 (for the existence of court discovery) is exactly
expected by theory, except NR D that had a negative parameter estimates and NR's C and E that
had no significant parameters estimated. It is because the option of these families and also by others
factor than sport. If D6 = 1, raises the intercept of the demand curve in 53, 29 and 85 students for
NR's F, G and N respectively.
Already the existence of pool (D7) leaves questions as to what type of pool there:
small (for the fun of children from preschool) or swimming lessons (also for adults). However what
the parameters estimated for this variable is clear is the concern about the existing security in the
PEI. In that showed significant parameter estimated only for NR A and B, wherein A is positive and
B is negative. That is, actually this factor does not influence the demand.
Science laboratory classes (D8) is a more qualitative factor of IE, since all the
studied schools have laboratory, but not all have regular classes on it. Whereas the maintenance of
these classes has a higher cost due to the price of materials. In that their existence raises the
intercept at 40, 167, 59, 26 and 100 NR's pupils for C, E, F, G and N. only insignificant NR D and
negative for NR A.
The student ratio per class (MT) presented the expected sign only for NR's B, F, G
and N where as is reduced by 1% the amount of average students in the classroom the demand
increases by 18% 8 % and 11% for the N's F, G and C respectively. Showing that these estimated
parameters are elastic. A clear concern with what people know as quality because several studies
show no relationship between academic achievement and class size. But regressions clearly show
consumer preferences. The other NR's had positive estimated parameters which may be a concern
about the prices charged or not significant.
As the average results achieved in ENEM (Ea) present estimates of the parameters
having a negative sign or not significant it is showing clearly the mistake of the population opt for
schools with advertisements in media claiming a certain quality in education but that, however, there
not have. Otherwise, the parameter estimated of this variable would be significant and positive.
(Although at the time (2003) the average achieved by schools was not disclosed in the media.)
However, this variable could just consider it as a proxy results in vestibular demonstrating the
mistake of the population.
Air conditioning (AR) presents estimates significant and positive parameters for all
NR's which put this feature as a sine qua non condition for the high school students study in which

7. 7
increase by 1% a number of air conditioners the demand students increases by 0.961%, 0.495%,
2.91%, 6.47%, 3% and 4.5% for the N's C, D, E, F, G and C respectively. Being estimation of
elastic parameters, except for NR D, makes it clear that teenagers prefer to study in schools having
air conditioning.
The temporal analysis of PEI IXAM we have a negative constant for the upper
classes G and F and N it is indicating that IXAM is not a school preference of these families. getting
the N off apparently these families act differently from other classes who were the only ones who
negotiated price rises the demand in a counterpoint of the constant. For them, the higher the
greater the guarantee of quality so it is the choice of these families. For classes with low income, the
price has a negative effect on demand.
The rise in fuel prices pushes up demand stimulated by G class only.
The number of students in other private schools has a positive effect on the
demand generated by classes B, C and F and negative effect by N.
The change in GDP per capita deserves a study because it has different effects
expected such as a decrease in demand generated by the classes A and F that could be because
probably when families of NR A change its level of class and the class F changes of schools.
However there is an increase in demand generated by classes B, E and G.
Enrollments in the public network have a negative effect on the lower classes,
putting the public system as a real competitor in these broad classes. Among the classes with higher
income, an increase in demand for public schools generates an increase in demand for private
schools, corresponding to the theoretical expectation that public schools become an evil and no
good.
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