how current economics situation influence students’ choice of major in the chinese university of...

8/7/2019 How current economics situation influence students choice of major in The Chinese University of Hong Kong

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How Economics Fluctuation Influence Students Choice of Major among The Chinese University

of Hong Kong

Author: Wong Man Hing

Student id: 1155002834

Guide Studies on current economic problems I

Section one

Introduction

In recent years, Hong Kong economy experiences much fluctuation. Some analysis said

that when economy is in prosperity, students are more likely to choose studying in business-

related major because of higher payroll expectation in this sector. At a result the admission score

of business-related major would soar, implying that more outstanding students are likely to work

in the sector after graduation. In austerity period, people are more likely choosing jobs that

provide more job security. As a result the admission score of those majors that train students for

particular careers that provide more job security is considered to rise during austerity. The main

goal of the paper is testing whether this phenomenon really exist by providing evident to indicate

how strong are the relation between economics fluctuation and students admission score in

different major.

Earlier survey conducted by Calkins & Weiki (2006) in the University of Height had found

that the expected marketability and expected income rank respectively the second and sixth that


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students concern when they were choosing university major. Further more, male weight expected

income a more influential factor than female. (Zafar, 2009). But no studies were conducted in

order to unveil how economics fluctuation influences students choice of university major and it

is what this paper aim to discover. Studies by Webbink & Hartog (2004) had found that in

general students predicted salaries and realized salaries rarely have large difference. But he did

not provide what factors indeed influence the forming of wage expectation. Berger (1998) found

that students would choose major that provide higher income expectation. Based on their finding

, I am going to test whether current economic environment affect major selection. With this

study, we could have better perspective on how students rearrange their choices of major amongeconomics fluctuation.

Apart from economic fluctuation, there is variety of factors that would influence students

choice of major such as the interest in the subject, peers influence, suggestion from high school

teachers. (Calkins & Weiki, 2006), Gender (Zafar, 2009), high school performance

(ThomasSource , 1985) and socioeconomics background (Blau & Ferber, 1991). Although those

factors swing the choice in a great extent (Canes & Rosen, 1995), it is difficult to estimate the

change in those factors using existing data (from instant, it is impossible to estimate the

difference on students interest among different years). If students interests were changing from

year to year, the admission score would plunge without any economic fluctuation. As a result, I

would concentrate on the impact of economics fluctuation.

In section two, I would introduce the methodology of this study. In section three, I would


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give a brief summary on recent economic performance. In section four, I would analyze the trend

admission score in business-related sector. In sector five, I would analyze the trend in non-

business major. In sector six, I would analyze the trend on admission score that had discover

strong relationship between economic performance and admission score, but in other university

in order to examine whether the existing linking are the result of the characteristic of those major,

or merely occasional cases. In section five, I would conclude this study.

MethodologyTo make comparison between the change on admission score in business-related major with other

degree progarmme. I would compare the admission score on Integration Bachelor of Business

Administration Programme (IBBA), Quantitative Finance, Medical Studies, Economics, and

Nursing in The Chinese University of Hong Kong (CUHK) so that we could have clear

perception about how admission score would change among economics fluctuation.

Admission score would be calculated by the follow formula:

ce is the marks of any Certificate Level Examination (CE) exclude Chinese and English.

ceeng is the marks of English in CE.

cechi is the marks of chinese in CE

ceii = 1

5

! + 2 ceeng + cechi8 x0.3

+

al ii = 1

2

! + aleng +1

2alchi

3.5 x0.7


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al is the marks of any AL subject except Chinese Culture and literature and Use of English

in Advanced Level Examination (AL)

aleng is the marks of Use of English in AL

alchi is the marks of Chinese Culture and literature

In every subject, examination grade would be transfer to numerical score. A get 5 marks, B

get 4 marks, C get 3 marks, D get 2 marks, E get 1 mark.

The admission score would be used to estimate students performance, although different major

would have difference in calculating its own admission, the formula is no disclosed, for that

reason, a general measurement would be adopted, so as to simplifying the analysis.

There are two indicators that I used to represent the economic fluctuation. The first one is

the year-on-year change in Gross Domestic Product in normal term (GDP) from 2004 to 2009,

which measure the general performance of Hong Kong economy. The second one is the Hang

Seng Index, which is a composite measurement on the performance of finance market. The first

could be seen as indicator of real sector; the second could be seen as indicator of financial sector.

In business sector could also be classified to those sector, for example, income of accounting

concentrated on IPO are consider closed related to financial market, but a manager of

merchandising industry are more related to real sector.After that, I would conduct regression analysis to reveal the relationship between those

indicators and admission score.


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Summary of Economics environment amid 2004 to 2010

From 2004 to 2010, General economics situation is pretty good. As showed in the Table

one, the nominal Gross Domestic Product (GDP) had risen from $1,291,923 millions to

$1,748,090 millions or 5.16% average increase. There was a slow down on growth in GDP in

2008(fron 9.5% in 2007 to 3.8% in 2008) and decrease in 2009 (-3.3%), but slump was

temporary as it reverted to grow in 2010(7.8%). We may say that although there was some

fluctuation in this period, but generally the economy was in prosperity.

However, If we look at data much related to performance in financial sector, the picture

would be quite specular. The Hang Seng Index, which measures the general performance of

Hong Kong Stock Marker, could divide to three periods among 2004 to 2010. In the first period

from Mar 2004 to the Oct 2007, it records a consecutive growth of a magical 139% (rising from

13120 to 31352). The second period is the occurrence of slump from Oct 2007 to Feb 2009, it

record a 59% slump. (falling form 31352 to 12811). The third period is a robust rebuttal from Feb

2009 to Mar 2010. (rising from 12811 to 20572)

Two indicator show a similar trend, so that either one could be used to compare with admission

score. Comparing two data would simplify the analysis and simple linear regression could be

adopted.

According to my hypothesis, it should be able to discover a pattern in the relationship

between admission score and change in GDP, In 2004 to 2007, admission score would have the

same trend (growing or dropping) due to the different wage expectation change in varies major.


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Admission score in business-related major should record growth and the major that could be

classified as Shelter should record a drop. In 2008 and 2009, the economic situation had reverse

to shrinking, Hang Seng Index drop 27% from Mar 2007 to July 2008. From July 2008 to July

2009,t he Hang Seng endure another fluctuation, the index drop to 12811 and climb back to

around 18000 point in July 2009. The last chance to rearrange major selection through the

JUPUS system is in August, Students witnessed the fluctuation may prefer the shelter rather

than high-risk business-related major.

Testing hypothesis in business sector

Table one and Table two show the admission score of different major between 2004 to

2009 by absolute value and year-on-year change respectively. If we plot the nominal GDP and

Admission score in Quantitative Finance together in a scatter diagram as showed in chart two.

We could find that they are positively related. In most of the case, when economy was expanding,

the admission score would increase at the same time.

To test their relationship, regression analysis is conducted as shown in table six. The regression

! ! is 0.79, indicating that 79% of the change in admission score could be explained by the

change of GDP.

However, if we expand the test to Economics and IBBA, this relationship is indeed not

clear. As Show in Chart three and Chart four. Most of the year, pattern on admission score in

Economics is basically following the trend, in which when economics expanding, admission


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score would increase. But indeed the relationship is very weak. In 2005, 2006 and 2010, when

nominal GDP record around 7% growth, change in admission score fluctuate between -4% to

7.5%. With a ! ! of 0.14 and 0.02 respectively, it is hard to comment that admission score are

influenced by current economics situation.

Ironically, when examining the data on IBBA, which should be credited for the most

business-oriented major for their concentrated studies in Accountancy, Finance, Decision Science

and Managerial Economics, Management, Marketing and STOT. (The Chinese University of

Hong Kong, 2010) As shown in chart four and table four, the admission score is decreasinginterruptedly except 2006. But after 2007, when the economy is in the most prospect period,

admission score revert to a drop. No pattern could be seen in the statistic, so it is even hardly

conclude that their have week relationship, but are independent.

Examining fluctuation of admission score in business-related major cannot discover an

obvious relationship between admission score and economic fluctuation in every major. The

picture is diverging, no consist result are discovered. However, hypothesis could be tested in

another way, which would be conducted in the next section.

Section

Some major, for example Medical Studies, Nursing would be consider as shelter when

economics is in austerity, due to its relatively high job security. Indeed, Hong Kong is lacking

doctors and nurses in public health sector (Sina, 2011), so studying in these major provide a


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nearly perfect probability to be hired after graduation. As a result, the admission score should be

soar during austerity.

Chart five show the relationship between admission score in medical studies to the year-on-year

change in GDP. In 2006 and 2007 where the economy is approaching its economics peak, the

admission score in medical studies drop 3.4% and 5.3% respectively. It could be interpreted it as

students changed their major from medical studies to other major due to soaring economy, but an

other time, results are not consistent. In 2005 and 2010, GDP rise 7% and 7.8% respectively

following by a 15% and 5% rise in admission score. This inconsistent phenomenon could not beexplained merely by expected payroll. Other factors should be considered to the main cause.

The relationship between admission score on nursing and change in nominal GDP (Chart

six) show a perfect case of students choosing major following to economics fluctuation as

quantitative finance did. But ironically the pattern is similar to the quantitative finance one, in

which when economy is growing, admission score would grow as the same time. In 2007, the

admission score record a amusing 27% rise. But in the following two year when economy slide, it

fall 21% and 7.5% respectively, in which indicating less competition in Nursing enrollment. With

a ! ! of 0.43, the data is showing that students do not saw nurse as a shelter. Contradictorily, It

likes a business-drove major than IBBA and Economics did.

Testing the credibility of current result

Within CUHK, admission score in quantitative finance and nursing show strong relationship with

economic fluctuation, but is that phenomenon exist purely due to the characteristic of the major?


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We would take statistic in other university into account in order to examine whether the pattern

on admission score in this two major were result of economic fluctuation.

Data of the cut-off score in nursing in The Hong Kong Polytechnic University (Poly) and

admission score in quantitative finance in The Hong Kong University of Science and Technology

(UST) are adopted. Table three shows the data and Chart seven show the relationship between

admission score in quantitative finance to GDP change.

No strong relationship between the admission score of quantitative finance and change of GDP

could be observed. As shown in chart seven, the ! ! is merely 0.2. It is much lower then what

were observed in CUHK.

Moreover, admission score of nursing in POLY was somehow independent with

economic fluctuation. It has very stable growth no matter what economic performance is.

Standard deviation of the admission score is 0.13.

Conclusion

Examining the statistic in CUHK, It is very strange that the data would provide such a

divergent result. Firstly, not all business-related major show a similar trend on admission score.

Secondly, The majors that provide training on careers that are classified as shelter do not show

an inverse trend with the quantitative finance which show a perfect example on a group of

students that choosing their major based on current economics environment.


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There is sight that some students adjust their major choice due to economics fluctuation

but most of them do not. Further more, doctor and nurse should not be classified as a shelter

career because no obvious trend of and inverse relationship between admission score and

economics growth.

The reasons behind that may simply because expected payroll is not the overwhelming

factor that influence students choices of major. Other factors are crucial in making decision. But

as I mentioned before, it is nearly impossible to examine it.

The second reason may be that students did not form their expected payroll base on

current years economics environment. Predicting future economics situation would require

adoption of a comprehensive analysis, in which many factors need to be taken into account.

Certainly current economics fluctuation is not the sweeping factor that influences prediction in

our case.

Although the hypothesis that is aimed to prove is rejected, through this study, we might

have better understanding on decision making of students. Current economic performance would

not be considered as major factors that influence expected payroll and more importantly, the

choice of university major if there are following studies on this filed.


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Reference

. (2006). . (10), 93-124.Zafar, B. (2009). College Major Choice and the Gender Gap. Staff Report (364).Webbink, D., & Hartog, J. (2004). Can Students Predict Starting Salaries? Yes! Economics of

Education Review , 23 (2), 103-113.Blau, F. D., & Ferber, M. A. (1991). Career plans and expectations of young women and men:The earnings gap and labor force participations. The Journal of Human Resources (16), 581-607.Calkins, L. N., & Weiki, A. (2006). Factors that influence choice of major: why some studentsnever consider economics. International Journal of Social , 33 (8), 547-564.Canes, B. J., & Rosen, H. S. (1995). Following in Her Footsteps? Faculty Gender Compositionand Women's Choices of College Majors. Industrial and Labor Relations Review , 48 (3), 486-504.The Chinese University of Hong Kong. (2010). CU-IBBA . Retrieved 3 10, 2011, fromDepaetment of Business Adminstration : http://www.baf.cuhk.edu.hk/program/ibba/course-selection/course-outline.htmlThomasSource , G. E. (1985). College Major and Career Inequality: Implications for Black Students. Journal of Negro Education , 54 (4), 537-547.


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Table one

Gross Domestic Product

Year Quarter

GDPAt current market prices

HK$ million Year-on-year % change2004 1,291,923 4.62005 1,382,590 7.02006 1,475,357 6.72007 1,615,574 9.52008 1,677,011 3.82009r 1,622,203 -3.32010p 1,748,090 7.8

Notes :Figures in this table are the latest data released on 23 February2011.r Revised figures.

p Preliminary figures.

Data obtained from Hong Kong Census and Statistic Department.

Table two:

Year Normal Value of Financing, insurance, realestate and business services ($ millions) year on year change(%)

2003 2510852004 266855 6.2807415822005 294260 10.269622082006 356371 21.10752396

2007 450989 26.550420772008 418389 -7.2285576812009 411635 -1.614287183

Data obtained from Hong Kong Census and Statistic Department

Table three: Admission score among different major between 2004-2010


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YEAR Quantitative Finance Cultural Studys IBBA English Education2010 4.15 2.8125 3.175 3.43752009 3.8625 3.2375 3.3875 3.0375

2008 4.1375 3.3875 3.5 2.752007 4.2125 3.2 3.675 2.32006 4.0625 2.875 3.7125 2.0752005 4.025 3.375 3.5252004 3.9625 2.6875 3.6625

YEAR Electronic Engineering Medicine Economics Chemistry2010 2.375 4.175 2.975 3.0252009 2.125 3.975 3.1 2.9252008 2.2125 4.225 3.175 2.7752007 2.175 4.05 3.1125 2.975

2006 2.1875 4.275 3.0125 2.56252005 2.2875 4.425 2.8 2.33752004 2.4375 3.8375 2.7375 2.5375

Data obtained from Hong Kong Census and Statistics Department

Table four: Year-on-year change in admission score between 2005-2010Year on Year Change on admission score

Year Quantitative Finance Cultural Studys IBBA English Education2010 7.443365696 -13.12741313 -6.273062731 13.16872428

2009 -6.64652568 -4.42804428 -3.214285714 10.454545452008 -1.78041543 5.859375 -4.761904762 19.565217392007 3.692307692 11.30434783 -1.01010101 10.843373492006 0.931677019 -14.81481481 5.3191489362005 1.577287066 25.58139535 -3.754266212SUM 5.217696363 10.37484595 -13.69447149 54.03186062

Year Electronic Engineering Medicine Economics Chemistry2010 11.76470588 5.031446541 -4.032258065 3.4188034192009 -3.95480226 -5.917159763 -2.362204724 5.4054054052008 1.724137931 4.320987654 2.008032129 -6.722689076

2007 -0.571428571 -5.263157895 3.319502075 16.097560982006 -4.371584699 -3.389830508 7.589285714 9.6256684492005 -6.153846154 15.30944625 2.283105023 -7.881773399SUM -1.562817871 10.09173228 8.805462151 19.94297577

Data obtained from Hong Kong Census and Statistics Department

Table five


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YearQuantitative Finance(UST)

Nursing (POLY) (cut-off score) GDP CHANGE

2010 3.32326284 2.564102564 7.82009 -7.02247191 2.631578947 -3.32008 8.868501529 2.702702703 3.8

2007 21.11111111 2.777777778 9.52006 -14.01273885 2.857142857 6.72005 0.964630225 2.941176471 7

Data obtained in JAPUS, cut-off score in Poly are calculated by the following method.

The cut-off score is the total score of all of your HKAL/AS and GCEAL/AS subject attainmentsusing the scale of A = 5, B = 4, C = 3, D = 2, E = 1 and applying a weighting factor of 0.6 to ASsubject attainments. For subjects with repeated attempts, the best grade is counted. A ratio of GCEAL/AS subjects to HKAL/AS subjects is also applied.(The Hong Kong Polytechnic University, 2010)

Table four Linear Regression analysis on relationship between admission score on quantitativefinance and GDP

Table six

Source | SS df MS Number of obs = 6-------------+------------------------------ F( 1, 4) = 15.76

Model | 91.8850552 1 91.8850552 Prob > F = 0.0165Residual | 23.3164216 4 5.8291054 R-squared = 0.7976

-------------+------------------------------ Adj R-squared = 0.7470Total | 115.201477 5 23.0402954 Root MSE = 2.4144

------------------------------------------------------------------------------Score | Coef. Std. Err. t P>|t| [95% Conf. Interval]

-------------+----------------------------------------------------------------GDP | .9357552 .2356898 3.97 0.017 .2813755 1.590135

_cons | -4.043099 1.581963 -2.56 0.063 -8.435331 .3491333------------------------------------------------------------------------------

Chart 1


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10,000.00

15,000.00

20,000.00

25,000.00

30,000.00

35,000.00

DATE

Hang Seng Index

Hang Seng Index

Linear (Hang

Seng Index)

y = 0.9349x - 4.0R = 0.7973

-8

-6

-4

-2

0

2

4

6

8

10

-4 -2 0 2 4 6 8 10

A d m i s s i o n S c o r e i n Q u a n t i t a t i v e F i n a n c e

Year-on-year changei in nominal GDP

Scatter diagram about Quantitative Finance


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Chart three

Chart four

v

Chart five

y = 0.3441x - 0.3391R = 0.1432

-6-4-202468

10

-4 -2 0 2 4 6 8 10 12

A d m i s s i o n s c o r e

Year-on-year change in Nominal GDP

Economics

y = 0.1452x - 3.0449R = 0.02619

-8

-6-4

-2

0

2

4

6

-4 -2 0 2 4 6 8 10 12

A d m i s s i o n S c o r e

Year-on-year change in Nominal GDP

IBBA


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Chart six

Chart seven

y = 0.54x - 1.153R = 0.09118

-10

-5

0

5

10

15

20

-4 -2 0 2 4 6 8 10 12

Y e a r - o n - y e a r c h a n g e i n n o m i n a l G D {

Year-on-year change in admission score

Medical Studies

y = 2.4422x - 9.7213R = 0.43103

-30

-20

-10

0

10

20

30

-4 -2 0 2 4 6 8 10 12

A d m i s s i o n S c o r e i n Q u a n

t i t a t i v e F i n a n c e

Year-on-year changei in nominal GDP

Scatter diagram about Nursing

GDP change

Linear Trend Line


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y = 1.2079x - 4.136R = 0.20337

-20

-10

0

10

20

30

-4 -2 0 2 4 6 8 10 12 A d m i s s i o n S c o r e

Year-on-Year change in nominal GDP

Scatter on Quantitative Financein UST

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