The determinants of success in educational choice

Economics Letters 0165.1765/93/$06.00

42 (1993) 411-417 0 1993 Elsevier

411 Science

Publishers

B.V. All rights

reserved

The determinants of success in educational choice G. Reza Arabsheibani Department of Economics,

University of Bristol, Alfred Marshall Building, 8 Woodland Road, Bristol, BS8 1 TN, UK

Received 17 August 1992 Final revision received 24 May 1993 Accepted 1 June 1993

Abstract This paper proposes that all students are not able to enter the specialisations that they initially desire, therefore research in educational (or occupational) choice must take this into account. In general ability, some school characteristics and sex significantly affect success in educational choice.

1. Introduction The analysis of educational choice has been the subject of much research. The early research in this field consisted of the analysis of enrolment in different levels of education as a function of the internal rate of return [Freeman (1971)]. Later this analysis was applied to curriculum choice by, amongst others, Koch (1972) and Greer (1977). Taking a different approach, Fiorto and Dauffenbach (1982) show that market forces do moderately determine curriculum choice. With the development of the econometrics of multivariate choice, researchers such as Fuller et al. (1982) have been able to develop choice models to explain college-going behaviour. However, most of these studies assume, probably because of the nature of their data, that the subject of study is actually what the individual chooses to do. It is the purpose of this paper to show that the desired field of study and actual field of study may be different. An attempt is made to identify the determinants of success (i.e. desired = actual) in curriculum choice.

2. Education

in Egypt

Modern education in Egypt follows a 6-3-3 pattern and is free, but a fee-charging private sector is available for those who wish to attend such schools. This sector is encouraged particularly in Cairo to the extent that in 1974 the Government started an aid package of fE 181,000 to help 169 private schools (Egyptian Gazette, 14 May 1974). The primary level takes six years and is compulsory. The preparatory level (or the lower secondary level) admits students between the age of 12 and 15. At the end of this level students take exams and only if they pass are they admitted to the secondary schools. There are two types of secondary education. General secondary schools run for three years, the first year of which is

412

G.R.

Arabsheibani

I Economics

Letters 42 (1993) 411-417

common to all students and in the final two years students either study arts or science. Technical education runs parallel to general secondary education for a period of three years and students can choose between industrial, agricultural or commercial technical schools. The primary objective of technical schools is to provide skilled manpower for the relevant sectors. At the end of secondary education students sit for a public examination and those who pass are awarded a diploma and can apply to one of the 12 Egyptian universities. Those who wish to study in English can attend the American University in Cairo, which is a fee-charging educational institution. Entry into universities is based on the result of secondary school and is done in various stages. Students are allowed to indicate their choices of subjects and the Ministry of Education announces minimum entry marks required for different subjects. In the first stage of admission those who satisfy entry requirements are admitted to the subject of their choice. Very often at the end of this stage most if not all places in the prestigious faculties (e.g. medicine) are taken up by more able students, i.e. those with higher marks. The remaining students are then admitted in various stages, again based on the marks and availability of places until all places are filled. It is therefore very likely that success in educational choice would strongly depend on secondary school certificate marks.

3. The model Discrete response models, educational choice being one of them, are assumed to arise from utility-maximising behaviour of individuals [see Amemiya (1981)]. Assume that the utility of an individual, when the desired field of study (D) is his current field of study (C), is given by u,,

= DL, + e,, )

where 0 denotes D = C, iJ is average utility associated with this state of the world and ei, is a random disturbance term. State 1 represents D # C and the average utility associated with this case is Vi, = uil + ei,. Our latent variable is defined as yi* = U,, - U,, > 0. Hence the value of our random observable variable, yi, is

yi=

1,

if y* >O

0,

ifyl*SO.

1

,

Assuming that U,, and U,, depend on certain explanatory variables we can write y* = x1!p + e*, where x,! is a vector of independent explanatory variables, p is a vector of coefficients and e* represents stochastically independent random errors. The probability that yi = 1 is given by Pi = Pr[y, = 11 = Pr[y* > 0] = Pr[e) Assuming that the probability estimate Pi as ’ P, =

distribution

> -XI /3] .

of e,? is determined

by a logistic

distribution

we can

1 1 + exp( -xl! p) ’

1 It should be noted that a limitation of the logit approach is that the closeness of desired and actual educational specialisation is not reflected in the dependent variable. My thanks to an anonymous referee for pointing this out.

G.R.

4. Data and empirical

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42 (1993) 411-417

413

results

The data used in this study were collected by the International Institute for Educational Planning in collaboration with the Council of Universities in Egypt [see Sanyal et al. (1982)]. Data on students were collected in the following way. All faculties and institutions were listed from the 12 Egyptian Universities (excluding the American University in Cairo). Sixty-nine faculties were chosen to represent a comprehensive cross-section of all faculties and 2660 questionnaires were sent out. Although there was a response rate of 85% (2273 replies), only 1935 replies were regarded as valid. In our analysis we use 1698 individuals all of whom gave complete information on all variables used in this paper, the most important of which is the desired subject of study as well as the actual subject of study. The explanatory variables used in our regression are as follows: GENR PUBL MARK SEX SEMP FOCl FOCZ FOC3 FOC4 FOC5

= = = = = = = = = =

1 if the individual attended a general rather than a technical school, 1 if the individual attended a public rather than a private school, marks obtained in secondary school certificate (range 0 to loo), 1 if individual is male, 1 if the individual intends to seek employment in the field of study, Father’s occupation: Professional + Managerial, Father’s occupation: Production + Agricultural, - _ Father’s occupation: Clerical, Father’s occupation: Independent, Father’s occupation: Other (omitted category).

Table 1 presents the result of logit regression for the whole sample. Out of a sample of 1698 students 768 were in a field of study that they desired to be. In other words, less than half of all students managed to enter subjects that they desired to study. Having studied in a private school and a technical school positively affect ‘success’, while family background and sex are insignificant. Marks positively affect the probability of success because it may be taken as a measure of ability as well as a method of rationing university places. Expectation of working in the field of study, which can be taken as a proxy for the commitment of individuals, also positively affects Pi. It is however possible that we might find a different pattern within broad educational specialisations. To look at this issue subjects were divided into five groups, medical science, science, social science, arts and other. The first four are ranked in that order according to students’ ability (MARK), demand for university places and social demand for educated manpower [for further explanation, see Arabsheibani (1991)]. The means and coefficients of the variables are presented in Table 2 and Table 3. As with the general case there is no clear-cut family background effect. Marks are very important in subjects that matter, i.e. where there is high student demand and therefore rationing. Technical education only matters in social sciences, which probably is the effect of three-year, business-oriented courses. Private education matters only in the case of medical science and arts. Expectation of employment in the field of study positively affects Pi in all subjects. The effect of sex is however different between subjects. Women are more successful in arts and social sciences and men in sciences. The coefficient of SEX in medical sciences is negative but insignificant. The pattern emerging is consistent with the occupational distribution of women in Egypt, studied by Arabsheibani (1990). There tends to be sex orientation in educational specialisation channelling women to certain feminine occupations. Medical sciences are an exception because of the nature of social demand. Since some muslim women only see female doctors there is a high demand for

G.R. Arabsheibani

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Letters 42 (1993) 411-417

female medical personnel. The relative success of women in this subject therefore can be explained by a combination of two effects. First, since medical occupations are open to women the field attracts the most able female student. Secondly, there may be positive discrimination in this case to make sure that supply meets social demand.

Table 1 Determinants

of the probability

of success

in education

choice

(all sample)” Coefficients

Mean

Variables

D=C

All GENR (Attending

general

PUBL (Attending

public school = 1)

DZC

0.94

0.92

0.96

-0.57 (2.52)

0.87

0.84

0.89

-0.55 (3.52)

73.00

74.72

71.60

0.03 (5.70)

0.62

0.63

0.60

0.14 (1.30)

0.72

0.79

0.66

0.61 (5.27)

0.19

0.21

0.17

0.18 (1.22)

0.15

0.15

0.14

0.13 (0.82)

FOC3 (Clerical)

0.19

0.17

0.20

-0.09 (0.65)

FOC4 (Independent)

0.14

0.13

0.14

-0.04 (0.21)

school = 1)

MARK (Out of 100) SEX (Male = 1) SEMP (Seeking employment field of study = 1)

in the

Father’s occupation : FOCl (Professional and Managerial) FOC2 (Production

and Agricultural)

-1.87 (4.26)

Constant

Log L: final

- 1105.90

Log L: initial

- 1158.90

Chi-squared

106.0

(9)

N ” Figures

1698 in parentheses

are t-ratios.

768

932

0.93 0.90 76.88 0.93 0.79 0.20 0.15 0.17 0.14 178 100

0.88 0.86 80.2 0.88 0.83 0.21 0.09 0.14 0.17 70 39.3 0.96 0.93 74.73 0.96 0.76 0.20 0.20 0.18 0.12 108 60.7

0.94 0.92 76.70 0.65 0.74 0.18 0.13 0.19 0.16 543 100

All

All

D=C

Science DfC

specialisations

Medical Science

for broad

a Variable definitions are the same as Table 1

GENR PUBL MARK SEX SEMP FOCI FOC2 FOC3 FOC4 N %

Vanables

Table 2 Mean of variables

0.94 0.91 80.10 0.69 0.81 0.22 0.14 0.16 0.14 250 46

D=C 0.94 0.93 73.9 0.62 0.67 0.14 0.13 0.21 0.17 293 54

DZC 0.95 0.85 70.54 0.57 0.68 0.1’) 0.15 0.20 0.13 335 100

All 0.91 0.85 73.45 0.52 0.77 0.20 0.21 0.18 0.14 118 35.2

D=C

Social Science

0.98 0.85 68.90 0.60 0.63 0.19 0.12 0.21 0.13 217 64.X

DfC 0.95 0.82 70.11 0.58 0.68 0.21 0.14 0.19 0.12 523 100

All

Arts

0.93 0.77 69.9 0.62 0.76 II.25 0.15 0.20 0.10 243 46.5

D=C

0.96 0.86 70.3 0.56 0.62 0.17 0.14 0.18 0.14 280 53.5

D#C

0.93 0.86 70.49 0.65 0.80 0.13 0.18 0.18 0.14 119 100

All

Other

0.93 0.86 70.61 0.63 0.83 0.10 0.18 0.18 0.16 85 71.4

D=C

0.94 0.88 70.20 0.70 0.70 0.18 0.18 0.18 0.08 34 28.6

D#C

416

Table 3 Determinants

G.R.

of the probability

Variable

Arabsheibani

of success

I Economics

in educational

choice

Letters

within

42 (1993)

broad

411-417

specialisations

Medical Science

Science

Social Science

GENR (Attending general school = 1)

-0.81 (1.15)

-0.17 (0.42)

~1.32 (2.25)

-0.47 (1.10)

0.30 (0.32)

PUBL (Attending public school = 1)

-1.18 (1.91)

-0.52 (1.50)

PO.05 (0.14)

-0.68 (2.77)

-0.55 (0.80)

MARK (Outof 100)

0.07 (3.40)

0.07 (6.52)

0.06 (4.09)

-0.007 (0.80)

0.005 (0.02)

SEX (Male = 1)

-0.29 (0.80)

0.42 (2.09)

-0.40 (1.90)

0.40 (2.13)

-0.56 (1.17)

SEMP (Seeking employment in the field of study = 1)

0.22 (0.52)

0.64 (2.93)

0.43 (1.94)

0.69 (3.47)

0.90 (1.74)

Father’s occupation: FOCl (Professional and Managerial)

-0.53 (1.15)

0.24 (0.84)

-0.01 (0.03)

0.57 (2.23)

-0.50 (0.80)

FOC2 (Production and Agricultural)

-0.44 (0.75)

0.12 (0.40)

0.78 (2.02)

0.15 (0.54)

0.03 (0.06)

FOC3 (Clerical)

-0.48 (0.95)

-0.34 (1.29)

-0.13 (0.40)

0.32 (1.22)

-0.05 (0.08)

FOC4 (Independent)

0.19 (0.40)

-0.31 (1.1)

0.48 (1.20)

-0.14 (0.40)

0.99 (1.26)

Constant

-3.63 (2.16)

-5.40 (6.04)

-3.80 (3.13)

0.48 (0.62)

0.71 (0.35)

Arts

Other

Log L: Final

-107.04

337.52

-198.11

-345.28

-67.70

Log L: Initial

-123.38

376.37

-232.20

-362.51

-82.48

N Chi-squared (9)

178 32.68

543 77.70

335 68.18

523 34.46

119 29.56

’ Figures in parentheses are f ratios.

5. Conclusions In this paper it has been shown that it is incorrect to assume that individuals studying a particular subject wanted to study it in the first place. In general students’ ability, dedication to the field of study and school characteristics affect the probability of success in educational choice. It is important therefore to distinguish between desired subject of study and actual subject of study. This issue may also be applied to the case of occupational choice. It is hoped that researchers in the future make an attempt to correct for this inconsistency.

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References Amemiya, T., 1981, Qualitative response models: A survey, Journal of Economic Literature 19, no. 4. Arabsheibani, G., 1990, Higher education and the occupational status of women in Egypt, Journal of Asian and African Studies, July. Arabsheibani, G., 1991, Supply and demand for graduates in Egypt, Higher Education Review 23, no. 3-4. Fiorto, J. and R.C. Dauffenbach, 1982, Market and non-market influences on curriculum choice by college students, Industrial and Labor Relations Review 36, no. 1. Freeman, R.B., 1971, The market for college trained manpower (Harvard University Press, Cambridge, MA). Fuller, W.C., C.F. Manski and D.A. Wise, 1982, Evidence on the economic determinants of post-secondary schooling choices, The Journal of Human Resources 17, no. 4. Greer, C.R., 1977, The relationship of returns to investment in undergraduate education and major area selection, Atlantic Economic Journal 5, no. 3. Koch, J., 1972, Student choices of university undergraduate major field of study and private internal rate of return, Industrial and Labor Relations Review 26, no. 1. Sanyal, B.C. et al., 1982, University education and the labor market in the Arab Republic of Egypt (Pergamon, Oxford).