impacts of urban economic factors on private tutoring industry

Impacts of urban economic factors on private tutoring industry

Kyung-Min Kim • Daekwon Park

Received: 6 March 2011 / Revised: 29 July 2011 / Accepted: 21 September 2011 / Published online: 14 October 2011

� Education Research Institute, Seoul National University, Seoul, Korea 2011

Abstract This paper investigates both supply (the num-

ber of employees in the PT industry and the number of PT

institutions) and demand (the number of middle and high

school students, grade 7–12). Panel data are used for this

research, making it possible to analyze market growth over

time since it contains both cross-sectional and time-series

information. Also this research sheds light on regional

differences (urban socio-economic factors) which influence

the growth of the PT market. Fixed effects and random

effects regression models are developed to analyze the

panel data. The purpose of this study is to analyze whether

the changes in the demand for PT affect the growth of PT

market between 2001 and 2006 in 25 gu (borough) of

Seoul. Urban economic and industrial structures and

income were controlled because they are known to influ-

ence the PT market along with demand for PT.

Keywords Private tutoring (PT) � Population �Manufacturing location quotient � Fiscal independence rate

Introduction

The prosperity of private tutoring (PT) industry is not news

anymore. In 2007, the estimated spending on private

tutoring was trillion won (approximately, $1.8 billion),

which amounts to 2% of GDP for Korea. The private

tutoring industry warrants careful investigation because it

accounts for a similar share of GDP as that of public

education. According to the OECD (2004), public educa-

tion spending as a share of GDP was 4.8 while PT

expenditures were 3.4% of GDP. The mean ratio of PT

expenditures among OECD countries was 1.3%.

The PT industry has been growing rapidly in Seoul as a

whole, as well as in the wealthiest neighborhoods in

southeastern area (Gangnam-gu, Seocho-gu, and Songpa-

gu). The number of people employed in the PT industry has

increased by 7% annually between 2001 and 2006

(KNSO). Korea Educational Development Institute (KEDI)

argues that the PT industry was the largest employer of

college graduate in 2009 (MEST and KEDI, 2009). For the

graduate of field of humanities and social sciences, cram

schools have been the biggest employer since 2005.

Allegedly, the reason for the demand in PT is the

unresponsiveness of public education (Kim 1998). When

public education no longer meets people’s longing for

better schools, parents choose tutoring as an alternative

supplement. The relationship between public education and

tutoring is analogous to tap water and mineral water (Kim

1998). That is, the city provides good quality tap water to

its citizens but also allows the selling of mineral water from

private vendors and the installation of purifiers at home.

Similarly, government and education officials must accept

private tutoring as a means for citizens to pursue better

goods and services. According to the Korean Consumer

Agency (1997), most parents agree to the efficacy of

tutoring (private and group tutoring as well as cram

schooling) and consider that tutoring contributes to

improvement in academic achievement.

Private tutoring is considered a double-edged sword

(Lee 2007). On the one hand, it serves a compensational

K.-M. Kim

Graduate School of Environmental Studies, Seoul National

University, 599 Gwanak-ro, Gwanak-gu, Seoul, Korea

D. Park (&)

BK21 Academic Leadership Institute for Competence-based

Education Reform, Seoul National University, 599 Gwanak-ro,

Gwanak-gu, Seoul, Korea

e-mail: [email protected]

123

Asia Pacific Educ. Rev. (2012) 13:273–280

DOI 10.1007/s12564-011-9192-7

function by providing supplementary education for those

with limited schooling opportunities and for individuals in

need of academic remediation. On the other hand, tutoring

enables high-achieving students to enhance their academic

interests and prepare for college. As such, private tutoring

can balance the educational attainment between high-pro-

file and low-profile students by meeting their educational

needs that are left untouched by the school system. How-

ever, it also triggers an equity issue, where spending on

tutoring increases with income.

Most of the previous research analyzing demand for PT

has focused on household-level expenditures on PT (Yoo

2006; Lee 2004; Choi 2003; Lee and Kim 2002), which

overhaul PT in micro level. However, there have been very

few efforts to analyze the demand and the supply of PT

industry in macro-level.

Yoo (2006) argues that the expenditures on PT increase

as parent education and income increase, based on KEDI’s

data of ‘‘estimating private tutoring industry scale.’’ He

also points out that families living in a wealthy area

(Gangnam) tend to spend more on private tutoring services.

Lee (2004) finds that the portion of expenditures on PT out

of household income has increased between 1998 and

2003. Based on his cross-sectional analysis, he finds that

expenditure on PT increases as household income grows.

Using data from Korea Consumer Agency (1997) and

Korea National Statistics Office (2008), Lee & Kim ana-

lyzed PT expenditures per household using OLS, Tobit,

and CLAD models. They find that the more the household

income grows, the more they spend on PT. Residents of

Seoul spend more than the residents of other larger

metropolitan cities, and the dwellers of small and middle

cities spend less than the larger cities. Choi (2003) also

finds that the expenditure on PT varies upon where people

live.

A voluminous body of international research on PT is

still fledging despite its pandemic appearance around

world, especially among developing countries (Bray and

Kwok 2003; Bray 1999). Common findings among coun-

tries are that expenditure on PT increases with higher

income and educational attainment (Bray and Kwok 2003;

Nath 2008). Biswal (1999) investigates the demand of PT

among developing countries and finds that low income of

teachers and lack of effective monitoring system of

schooling trigger the demand for PT.

Previous research has focused on the family wealth and

residential location. Analysts have found higher PT par-

ticipation rates among higher income and more prestigious

residential locations. However, such research is confined to

the influence of household’s structural characteristics on

PT expenditure; it overlooks the macro-level analysis of

the growth of the PT market itself. Also, most researchers

conduct cross-sectional analyses rather than time-series

analysis, and thus pay less attention to the changes in the

supply and demand of the PT market over time. For

instance, in order to measure demand for PT, one relevant

proxy can be the number of students in a city. However,

since previous researchers tend to conduct individual-level

analyses, they typically fail to measure the demand as a

whole, resulting in the number of students, one of signifi-

cant variables, have not been appealing.

This paper investigates both supply (the number of

employees in the PT industry and the number of PT

institutions) and demand (the number of middle and high

school students, grade 7–12). Panel data are used for this

research, making it possible to analyze market growth over

time since it contains both cross-sectional and time-series

information. Also this research sheds light on regional

differences (urban socio-economic factors) which influence

the growth of the PT market. Fixed effects and random

effects regression models are developed to analyze the

panel data. The Hausman test is used to choose the best

fitting model, and the results of the regression analysis are

described based on the model with the best fit.

The purpose of this study is to analyze whether the

changes in the demand for PT affect the growth of PT

market between 2001 and 2006 in 25 gu (borough) of

Seoul. Urban economic and industrial structures and

income were controlled because they are known to influ-

ence the PT market along with demand for PT.

The overview of PT market of Seoul

This paper defines the supply of PT as the number of

employees or PT institutions, and the demand for PT as a

number of middle and high school students.

Table 1 shows the increase of employees between 2001

and 2006. The table shows that there has been a continuous

increase in annual growth; there are 41% more employees

in 2006 compared to that of 2001. Examining Gangnam-

gu, Songpa-gu, and Seocho-gu, the ‘‘big three’’ PT firms

Table 1 The increase trend of PT industry employee

Seoul Gangnam Seocho Songpa

2001 48,354 5,296 3,179 3,951

2002 53,992 6,113 3,064 4,193

2003 56,431 6,078 3,422 3,904

2004 61,532 7,164 3,636 4,199

2005 65,567 8,756 4,210 4,598

2006 67,990 7,934 4,639 4,832

Avg. growth rate 7% 9% 8% 4%

Korea national statistics office (KNSO), various years

274 K.-M. Kim, D. Park

123

also show the growth in the volume of the PT industry. The

average annual growth rate of PT industry employees is

7.1% during the study period. Jungrang-gu, one of the

poorest neighborhoods in Seoul, has the lowest increase

rate of -2% and Yangcheon-gu and Gwanak-gu have the

highest growth, averaging 13% annually rate. Gangnam-gu

and Seocho-gu have ratios of 9 and 8%, respectively, and

they are higher than the average growth rate of Seoul,

7.1%; only two gu, Jungrang-gu and Yongsan-gu, have

negative (-) growth rate while the other 23 gu show

positive (?) growth rates.

Examining the growth rate of PT vendors shows similar

trends as that of the PT industry employees. Average

annual growth is 9%. At an average rate of 3% per year,

Jungryang-gu has the smallest growth rate, while the

highest is Yangcheon-gu, at 15%. Every gu of Seoul shows

positive (?) growth, and Gangnam-gu and Seocho-gu show

11 and 10%, respectively. These descriptive statistics,

shown in Table 2, illustrate the rapid growth of PT markets

all around Seoul from 2001 to 2006.

Tables 1 and 2 show the growth of supply of PT

industry of Seoul. However, Table 3 confirms the declining

trend of demand of PT industry. The number of middle and

high school students (grade 7–12) has been reduced dras-

tically between 2001 and 2003 and increased moderately

since 2004. But Gangnam, Seocho, and Songpa, known as

the Big 3 of PT industry which are expected to have the

largest demand of PT, have had continuously declining

enrollments of middle and high school students from 2001

to 2006. In the case of Gangnam-gu, the number of middle

and high school students decreases from 51,989 (2001) to

48,511 (2006). This is a 7% loss among its 7–12th graders.

Overall, Table 3 shows that PT enrollment has declined by

more than 5% among middle and high school students in

Seoul.

The number of 7–12th grade students in Seoul has been

decreasing at an average annual rate of 1.1%. Among the

25 gus of Seoul, 18 gus show negative (-)growth rates.

The gus with positive growth are Guro (0.2%), Seongdong

(0.7%), Yangcheon (1.4%), and Dobong (2.7%). Gangnam

(-1.4%) and Songpa (-1.6%) have growth rates that are

lower than the average rate in Seoul, -1.1%, suggesting

that the Gangnam and Songpa are losing students faster

than the average across Seoul.

Every gus show the increase in the number of PT

industry vendors, and 23 out of 25 show an average annual

increase in the number of PT industry employees but the

student enrollment rate in of 21 out of 25 gus has been

decreasing. This contrast suggests a disparity between the

public demand and market response. While the supply of

PT industry has been thriving recently, the demand of PT

has been showing negative way; PT market grows without

the supply of certain amount of customers and students.

This study begins from this disparity.

Analysis

Selection of variables

The focus areas of this research are the 25 gus (boroughs)

of Seoul and the period is from 2001 to 2006. A balanced

panel was developed to conduct this analysis, which

includes time-series data on 25 gus for 6 years for a total

sample size of 125. To pursue the objective of this study,

analyzing the factors that influence the supply of PT, the

density of PT industry vendors and the density PT industry

employees were included as dependent variables. For

independent variables, density of middle and high school

students, the log of the number of middle and high school

students, density of population, location quotient of man-

ufacturing employees, and ratio of financial independence

were considered (Table 4).

The density of middle & high school students represents

the demand for PT, the number of students divided by the

area of each gu. The log of the number of middle and high

school students reflects the size of PT demand.

Population density reflects the size of the economy of

each gu, and the location quotient of manufacturing

employees indicates the extent of maturation of

Table 2 The increase trend of PT vendors


2001 6,274 632 343 526

2002 6,985 716 384 559

2003 7,831 784 437 617

2004 8,108 835 434 612

2005 8,984 993 501 688

2006 9,668 1,048 559 746

Avg. growth rate 9% 11% 10% 7%

Source: KNSO, various years

Table 3 Enrollment of middle and high school (Grade 7–12)


2001 775,029 51,989 31,561 54,848

2002 739,412 49,956 30,304 52,502

2003 726,013 49,906 29,878 51,155

2004 726,708 49,473 30,364 50,914

2005 732,211 49,009 30,321 50,650

2006 734,131 48,511 30,237 50,635

Avg. growth rate -1.1% -1.4% -0.8% -1.6%


Impacts of urban economic factors 275

123

manufacturing industry. If location quotient (LQ) of man-

ufacturing employees is high, it shows that the gu has high

ratio of manufacturing industries; if an area has higher LQ

of manufacturing employees, the area may be considered to

less attractive area to live, since people dislike to live near

manufacturing factories. It may generate less attractiveness

for the service industry to be located in such an area.

Self-reliance ratio for the local government finance is a

proxy variable for income data of each gu. This quotient is

a good supplement index because it shows the gap of the

income among gus.

Selection of regression model

Panel data were used to estimate the effects of demand on

changes in the supply of PT industry in each gu of Seoul.

The analysis is conducted using Fixed and Random Effects

Models.

Let us assume following regression model

Yit ¼ bXit þ uit: ð1Þ

Yit is the density of PT industry employees or vendors of

gu(i) in year (t). Xit is the vector of independent variables,

including student density, population density, self-reliance

ratio for the local government finance, the location quotient

of manufacturing employees, and number of students of gu

(i) of year (t). uit is independent and identically distributed

error term.

Panel data show the characteristics of both cross-sec-

tional and time-series data. Like other cross-sectional data,

this panel data also include the differences between gus.

Importantly, unobservable, time-invariant factors are most

problematic in time-series analyses. If such unobservable

variables exist, the regression model will take the form:

Yit ¼ bXit þ ni þ eit: ð2Þ

Here the error term from (1), uit, can be expressed as two

factors to demonstrate the problem presented by

unobserved variables (2). The unobserved variable is

represented by ni, and the error term is noweit. If ni

exists in the model, the independent variables will have

biased coefficients. For this reason, the regression model

must deal with ni properly, and there are two ways to do

this: Fixed Effects Models and Random Effects Models.

In a Fixed Effects Model, ni is not influenced by time,

but it represents the unknown, invariant characteristics of

each area.

The regression model can be constructed after measur-

ing the mean of each variable as follows:

�Yit ¼ b �Xit þ ni þ �ei: ð3Þ

From Model 3, the mean of ni will be ni itself. Thus, by

subtracting Model 3 from Model 2, variable ni will be

deleted. The problem of unobserved variable bias can be

solved through this process, and this model is called a

Fixed Effects Model (Greene 1993).

In a Random Effects Model, ni is considered as a ran-

dom variable—and thus independent of Xit—rather than an

unknown constant that is correlated with Xit. So ni is

referred to as independent and identically distributed (i.i.d),

and it is not related to other independent variables and ei.

With such a model, the regression coefficients can be

calculated by a Generalized Least Squares (GLS) method

in a Random Effect Model.

This paper also attempts to eliminate the possibility of

reverse causality. If the dependent variable is the growth

of PT industry at time t and the independent variables of

interest are students’ density and urban environment at

time t, then the distribution of the dependent variable will

influence the independent variables and vice versa, leading

to problems with identifying the true causal effects of the

independent variables. The analysts choose the time of

independent variable of t-1 instead of t, to exclude the

possibility of reverse causality.

A Hausman test was conducted to find out which model

fits between Fixed Effects Model and Random Effects

Model and is presented as follows (Hausman 1978)

H ¼ Tðbfe � breÞ0Varðbfe � breÞ�1ðbfe � breÞ:

T is number of data of sample, (bfe) is regression

coefficient of Fixed Effects Model and (bre) is coefficient

of Random Effects Model. The Hausman test follows a

Chi-square distribution. A null hypothesis (H0), (bre) is

consistent and efficient while (bfe) is consistent but

Table 4 Analysis variables

Variables Source

PT employee

density

Census data PT employee/M & H

student enrollment(Code: O8093)

PT vendors

density

Census Data PT Vendors/M & H

student enrollment(Code: O8093)

Students

density

Census data M & H student/(km2)

area of Gu

Population

density

Census data Population/(km2)

area of Gu

Location

quotient of

manufacturing

employees

Census data (Manufacturing employee of

Gu/manufacturing employee

of Seoul)/(all industry

employee of Gu/all industry

employee of Seoul)

(Code: D)

Student

enrollment

Census data Log of M & H student

enrollment

Financial

independence

Ministry of

public

administration

and security

Financial independence

rate (%)


123

inefficient. So, a Random Effects Model will be more

fitting if H0 is selected, while a Fixed Effects Model will

be chosen if HO is rejected because is consistent.

Results and discussions

This paper estimates four models to analyze the growth of

PT industry. In model 1, PT industry employee density is

set as the dependent variable, and student density is set as

the independent variable. Model 2 sets PT industry

employee density as dependent variable and the logarithm

of the number of students as the independent variable.

Model 3 uses PT industry vendor density as the dependent

variable and student density as the independent variable of

interest. In model 4, PT industry vendor density is set as the

dependent variable while the log of the number of students

is set as the independent variable.

There are possibilities of time effects on the PT market

between 2001 and 2006 because of domestic and interna-

tional economic fluctuations. So, year dummy variables are

used control for time effect in every model.

PT industry employee density can be explained as fol-

lows: with the characteristics of the panel data, the increase

of PT industry employee density over time implies that

incumbent PT vendors prosper or new PT vendors open

their business at the area. So, finding that the independent

variable has a statistically significant effect on PT industry

employee density would suggest that PT vendors are basing

location on demand for their services.

Tables 5, 6, 7, and 8 show the finding from Model 1.

Variables from the Fixed Effects Model and Random

Effects Model have equal signs but the statistical signifi-

cance varies of some variables differ between models.

With a p-value of 0.66, the Hausman test indicates that

the null hypothesis cannot be rejected, so a Random Effects

Model is selected as a better fitting model compared to the

Fixed Effects Model, and the analysis is conducted through

observing the coefficients in the Random Effects Model.

Student density, population density, location quotient of

manufacturing industry, and financial independence ratio

are all statistically significant. If the population density

increases at time, t-1, then the PT industry employees at

time t increases. As population density serves as a proxy

Table 5 Model 1: education demand (student density) versus PT

employee

Fixed effect

model

Random effect

model

Coef t value Coef t value

Student density -0.021 -0.99 -0.025 -2.21*Population density 0.374 1.17 0.255 2.35

Manufacturing location

quotient

-0.345 -2.99 -0.018 -3.01

Fiscal independent rate 0.103 1.17 0.065 3.43

2003 0.004 1.45 0.003 1.4

2004 0.01 3.27 0.01 4.18

2005 0.015 5 0.014 5.81

2006 0.016 5.99 0.015 6.22

Constant 0.048 0.8 0.044 2

Sample size 125 125

R2 0.5 0.48

Hausman 5 p-value: 0.66

* Unit of population density: million

Table 6 Model 2: the effect of the number of students (education

demand) on PT employee

Fixed effect value Random effect value


Ln student -0.029 -0.91 -0.004 -0.33

*Population density 0.33 1.08 0.11 1.2

MLQ -0.034 -2.93 -0.018 -2.61

FI 0.034 1.18 0.053 2.74

2003 0.004 1.35 0.004 1.95

2004 0.01 3.03 0.012 4.65

2005 0.014 4.65 0.016 6.4

2006 0.016 5.7 0.017 6.96

Constant 0.32 1.04 0.085 0.65

Sample size 125 125

R2 0.5 0.49

Hausman 4.62 p-value: 0.71


Table 7 Model 3: the effect of student density (education demand)

on PT venders

Fixed effect model Random effect model


Student density 0.0007 0.39 -0.0002 -1.48


MLQ -0.003 -2.76 -0.002 -3.19

FI 0.003 1.26 0.005 2.52

2003 0.001 6.4 0.001 5.96

2004 0.002 7.16 0.002 7.56

2005 0.003 11.65 0.003 12.23

2006 0.003 15.28 0.003 15.27

Constant 0.002 0.48 0.004 1.83

Sample size 125 125

R2 0.8 0.79


* Unit of student density: 1,000; unit of population density: million


123

for the size of the region’s economy, areas with larger

boundaries of economic influence have bigger increases in

PT industry employees.

Manufacturing location quotient, which shows the

maturation of industry, shows that the increase of location

quotient at time t-1 results in a decrease of PT industry

employees at time t. Thus, regions with higher concentra-

tions of manufacturing, the lower maturation area, cause a

decrease in the number of PT industry employees.

The increase of financial independence of time t brings

the increase of the number of PT industry employees. As

discussed earlier, the increase of financial independence

has positive correlation with the increase of household

income. So it can be interpreted that the income level has

positive influence on the growth of PT industry.

Population density, manufacturing location quotient,

and financial independence rate are all statistically signif-

icant and show expected signs. However, student density,

the direct indicator of demand, shows negative (-) signs in

both models and is statistically significant in the Random

Effects Model. This finding is in contrast to the public

recognition of PT based on economic principles; the

increase of demand (number of students) raises the supply

(number of PT industry employees). The finding of this

model shows that the supply of PT industry, growth of PT

industry employees, responds to urban economic variables

rather than student density, the direct demand of PT

activity. This means that, over time, PT industry employees

increase in districts with larger economies of scale, higher

income residents, and lack of manufacturing base con-

trolling for time.

Model 2 uses the log of the number of students instead

of student density. This variable serves as a direct indicator

for the size of the educational demand or market. A Haus-

man test confirms that the Random Effects Model is a

better fitting model, given the structure of the data.

The log of the number of student and population density

is not statistically significant. As discussed above, the

demand for PT is expected to bring the increase of PT

industry employees. However, the lack of statistical sig-

nificance of the student number means that supply of PT

industry is not driven by demand. Manufacturing location

quotient and financial independence rate are statistically

significant and have expected signs.

Model 3 and Model 4 use PT industry vendor density as

a dependent variable instead of PT industry employee

density. A Random Effects Model is selected for both

models.

In Model 3 and Model 4, population density, manufac-

turing location quotient, financial independence rate are all

statistically significant and have the expected signs. Similar

to Model 1, a region with a larger economy (higher pop-

ulation density), larger industrial base (lower manufactur-

ing location quotient), higher income (higher financial

independence rate) has more PT industry vendors.

But the growth of PT industry vendors is not related to

the indicators for PT demand used in these analyses.

Conclusion

Based on regression model, this study reaches three

conclusions.

First, contrary to popular opinion, demand for PT, stu-

dent population has a negative or not statistically signifi-

cant effect on PT market growth. As the descriptive

statistics shows previously, the number of students in the

big three, Gangnam, Seoucho, and Songpa-gu, has been

decreasing. Conventional wisdom holds that diminishing

market demand usually brings the downsizing of market

supply. However, the findings show that the growth of the

PT market is not a product of the increase in number of

students. Second, the growth of the PT market depends on

residents’ income level or local economic vitality, like the

independence rate of local finance, location quotient of

manufacturing employees. Third, referring to the positive

effect of higher population density on the growth of the PT

market, it grows with the expansion of the volume of local

economy. Summarizing the result of the analysis, the

location of PT market is more likely to rely on the eco-

nomic conditions (e.g. scale of economy and wealth of the

area) rather than on the possible demand of PT (the number

of students).

According to KNSO (2008), among families earning

more than 7 million won (around seven thousand dollars)

per month, 93% of middle school students and 80% of high

Table 8 Model 4: the effect of number of students (education

demand) on PT venders

Fixed effect model Random effect model


lnStu Popul 0.0003 0.12 0.0005 0.4


MLQ -0.018 -2.61 -0.002 -2.72

FI 0.053 2.74 0.004 2.12

2003 0.005 1.95 0.001 6.75

2004 0.012 4.65 0.002 8.24

2005 0.016 6.4 0.003 13.15

2006 0.017 6.96 0.003 16.48

Constant 0.085 0.65 -0.001 -0.11

Sample size 125 125

R2 0.8 0.8




123

school students are participating in PT programs. In con-

trast, among families earning less than one million won per

month, only 31 and 20% of students, respectively, are

participating in PT. This observation suggests that the

location of the PT industry depends on marketability,

where wealthy family lives, rather than the possible

demand among all students. The students from the higher

SES family are more likely to consume PT service rather

than the kids from lower SES parents. For this reason, PT

service vendors are prone to locate their business at the

residence of higher SES families if location costs are equal;

as private entrepreneurs who seek profits, they prefer a fish

market rather than fishing ground.

It is natural that PT vendors open their business where

students exist at the early stage, the supply demand. But

when the business has matured, the venue changes; supply

begins to attract demand. When PT vendors respond to the

needs of the supplemental tutoring of their neighbors

within their primary area business, their scope of influence

is geographically limited. In the event that their business is

thriving and their reputation is spreading, their influence

crosses borders. This popularity can lead to synergy with

the urban conditions of Seoul, such as high population

density, well-developed transportation system, and a zeal

for education among parents. PT vendors can attract the

students all around the city, even all around the nation and

from abroad.

Some thriving PT vendors recognize the value of their

brand, so they open their new branch at a wealthy location.

As they operate school buses to offer more accessibility,

the location of the PT vendor’s business can extend beyond

the proximity of their immediate location. Despite the

decrease of the student population, PT vendors are creating

new markets and nurturing the environment in unprece-

dented way in education markets.

Global financial giants are throwing huge investments

into the PT industry in Korea. As a result, PT service

vendors will devote themselves to maximizing monetary

benefits rather than providing good service of supplemen-

tary education (Aurini, 2004).

PT as a complement good for public education has been

vocal recently. But this paper finds that this argument

remains true on the lands of the haves. Table 9 shows gu of

Seoul’s density of PT vendors (number of PT vendors/area

of gu) and employees (number of PT employees/area of

gu). In case of vendors, mean, max, and sd are increasing.

For employees, max increased until 2005. Continuous

increase of sd shows that the growth of PT industry of each

gu has been differentiated; it means that the gap between

each gu has been exacerbated. As shown above, PT

industry has been thriving based on the neighborhood’s

economic prosperity. So the expansion of PT industry will

bring adverse impact on the students from low income

families.

Some assertions to nurture PT industry as an alternative

to revitalize unsatisfactory public education, like creating

cram schools village in Pangyo New Town in Metropolitan

area have to be reconsidered. As the PT industry is

responsive to the level of household income and/or scale of

economy of neighborhood, the demand for PT has little to

do with bringing PT vendors to its vicinity. This finding

shows that public recognition toward PT has disparity with

the reality of PT industry.

PT vendors are called ‘‘supplementary education ser-

vices’’ but this study finds that the function of PT vendors

is not supplementary but polarizing in urban area, in case it

has thriving PT industry and well-developed transportation

system. PT vendors can supplement or promote prior

learning of children of high SES families but not to the

children who needs supplement in real.

References

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Table 9 The increase trend of PT vendors and PT employees density

PT vendor density PT employee density

Year Mean Min Max SD Mean Min Max SD

2001 6 1.7 16 3.4 49 16 134 27

2002 7.1 2.1 18.1 3.8 55 19 155 31

2003 7.9 2.3 19.8 4.2 57 22 154 31

2004 8.2 2.1 21.1 4.4 61 19 181 37

2005 9.1 2.4 25.1 5.2 66 19 221 44

2006 9.8 2.6 26.5 5.7 69 22 201 42



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impacts of urban economic factors on private tutoring industry

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