3.DATA

As outlined in Chapter 2 the estimation of the input-output model and the making of projections of the number of students at various stages of the higher education system requires data on:

It is possible to estimate the model with data from just two consecutive years. In this report we use course enrolment in higher education and school enrolment data from 1993 and 1994, and course completions data in higher education from 1993. The population estimates are for 1993 and 1994 and projections for 1995 to 2001. The three data sets are described and their initial analysis reported in the following sections.

Data on course enrolment and completions is used to estimate the matrix of transition proportions. It was obtained from unpublished records kept by DEET. Course enrolment and completions data are stored on separate files as aggregated records. These aggregated files are derived from unit record files provided by each university funded by DEET. DEET releases only aggregated data to safeguard confidentiality of individual students.

Specially compiled course enrolment files for 1993 and 1994 and a course completions file for 1993 were obtained from DEET. These files are different from the files that DEET normally makes available in one significant respect. In the specially compiled files the course commencement date (month and year) of students is included instead of just the year since commencement (one, two, three or three plus).

A vast amount of information is stored on each file. However, not all the variables that are included in the enrolment files are included in the completions file. For example, variables indicating whether a student commenced the course as a school-leaver or not and that indicating the mode of attendance, that is full-time, part-time or external, are not included in the completions file. This means that the analysis that can be performed will be limited by what is contained in the completions file.

The course enrolment files contain information on thirty two variables, and the course completions file on only thirteen of these. At this stage the analysis is restricted to looking at the system at the national level, although the model is equally applicable at the state/territory level. Only the following seven variables are used to estimate the input-output model:

The 1993 enrolment file has over 355,000 records while the 1994 file has over 370,000 records. The 1993 completions file has just under 86,000 records. SAS software was used to read and interrogate the data files on an Alpha machine. A variable giving the year of enrolment in the system since course commencement, Y, was generated from the course commencement date. In order to be consistent with DEET's definition of a commencing student the following definition was used for this variable:

where Ref_year is the year of the data file, Year is the year in which the course was commenced and month is the month in which the course was commenced. It should be noted that DEET defines a commencing student as one who is in his/her first year of a particular course at a particular institution. This means that a student who for some reason changes course or begins another degree after having completed one, is classified as a commencing student for a second time even though he/she may not be new to the university or faculty.

Preliminary analysis of the data revealed that students' age ranged from zero to ninety-eight! There is no reasonable explanation than coding error for the existence of students of age zero or 98. There was also some doubt about students who were in their nineties, and therefore, anybody whose age was zero or over 89 years was excluded from the analysis. This process resulted in the removal of 240 students in the 1993 enrolment file, 54 in the 1994 enrolment file and 25 in the 1993 completions file.

A value of negative one for Y in the 1993 course completions file implies the commencement date of between April 1994 and December 1994 for these students. A coding error is the most likely explanation for such a value of Y. There were a number of students whose commencement and completion times were between April 1993 and December 1993 (Y equal to zero). Similarly Y equal to zero in the enrolment file for 1994 indicates that some students' commencement date is after March 1994.

In order to be consistent with the data in the enrolment file for 1993, students for whom the variable Y is less than one are excluded from further analysis. This resulted in the removal of 122 students (109 undergraduates and 13 postgraduates) from the 1994 enrolment file and 598 (300 undergraduates and 298 postgraduates) from the 1993 completions file. Since the number of such students is relatively small, their exclusion is unlikely to significantly affect the estimation of the models. Moreover, by not excluding them the model building exercise can become unnecessarily complex.

According to DEET (1995a) there are two categories of undergraduates-Bachelor and Other Undergraduates. Six courses of varying length are included under these two categories. We have defined undergraduates as those students enrolled for the following three courses:

These three courses generally take three to four years of full-time study and tend to form a homogeneous group among the undergraduate courses. They comprised over 95 percent of all undergraduate enrolment in 1993 and 1994. A number of diploma courses have been converted to Bachelor courses in recent years, most notably in the nursing and teaching area.

The number of years of enrolment has six categories-one, two, three, four, five and six or more.

These fields are the same as those defined by DEET (1995a) with two exceptions. First, Education has been split into two fields of study-initial training and the other. The initial training part of education is of importance in its own right because a high proportion of the supply of new graduate teachers come from this category. This field will be referred to as Education (I) and the rest of education as Education (O). Secondly, Medicine (not medical science) and Dentistry (not dental therapy) has been taken out of Health and combined with Veterinary Science to form one field which from now on will be referred to as Medicine. Combining Medicine, Dentistry and Veterinary Science into one field makes statistical sense because all three courses generally take about five years to complete.

This was done for each broad field of study. Moreover, the gender balance in each group defined by age and field of study, and number of years of enrolment and field of study, was investigated. In general, there was no significant difference between 1993 and 1994 in the age profiles, and the distribution by number of years of enrolment. Thus, only the analysis for students enrolled in 1994 is reported.

The age profile of students enrolled in undergraduate courses in 1994 is given in Table 3.1. The Australian category does not include students who are full fee-paying and from overseas. Only Australian students are included in the data for each broad field of study. Overall 34 percent of male students are under 20 years of age compared to 37 percent of female students. Similar percentages in the 20-24 age group are 41 and 35, and in the over 24 age group 26 and 28, for male and female students, respectively.

	All		Australian		Agriculture		Architecture		Arts		Business		Education (I)
*Age in Years*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	7	8	7	9	6	9	6	8	6	8	7	9	5	9
18	13	14	14	15	13	16	13	16	12	14	14	15	12	16
19	14	15	14	15	16	17	14	17	13	14	14	16	15	18
20	13	13	13	13	13	14	13	13	11	11	12	13	15	16
21	11	9	11	9	11	10	11	11	9	7	10	9	12	11
22	8	6	7	6	7	5	9	10	6	5	6	6	7	5
23	5	4	5	4	4	3	7	7	5	3	4	4	4	3
24	4	3	3	3	2	2	4	5	4	2	4	3	3	2
25-29	11	9	10	9	10	7	11	7	12	9	12	10	9	6
30-34	7	6	7	7	7	6	5	4	8	7	8	6	6	5
Over 34	8	12	9	13	11	9	5	3	15	19	9	8	12	9
Total	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Under 20	34	37	36	38	34	43	34	40	31	36	35	41	32	43
20-24	41	35	39	34	37	35	44	46	34	29	37	35	41	37
Over 24	26	28	25	28	28	22	22	14	35	35	29	24	27	20

	Education (O)		Engineering		Health		Law		Medicine		Science
Age in Years	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	0	1	9	12	6	7	5	6	7	7	9	13
18	1	1	17	19	13	14	8	11	11	12	16	19
19	1	1	17	20	14	15	11	12	14	15	16	18
20	2	4	16	18	12	12	11	13	14	14	14	15
21	5	8	13	13	9	7	11	13	16	15	11	10
22	5	8	8	7	7	5	10	10	14	14	7	5
23	5	7	5	3	5	3	6	6	9	8	4	3
24	5	7	3	1	4	3	3	3	4	3	3	2
25-29	23	18	7	3	11	10	12	9	6	6	9	6
30-34	16	11	3	1	8	9	8	6	3	3	5	4
Over 34	37	35	2	1	10	15	14	11	1	1	6	5
Total	100	100	100	100	100	100	100	100	100	100	100	100
Under 20	2	3	43	51	34	36	24	30	32	34	41	50
20-24	22	34	45	43	37	30	42	44	57	55	39	36
Over 24	76	64	13	6	30	34	34	26	10	10	20	15

	All		Australian		Agriculture		Architecture		Arts		Business		Education (I)
Age in Years	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	0	0	0	0	0	0	0	0	0	0	0	0	0	0
18	0	1	0	1	0	1	0	0	1	1	1	1	0	1
19	7	12	7	12	4	6	8	10	8	13	10	14	6	16
20	17	21	18	22	17	21	17	20	18	23	21	26	18	27
21	19	17	19	17	21	29	18	21	16	16	20	21	18	18
22	13	10	13	9	14	15	13	13	10	8	11	10	11	8
23	9	6	8	6	8	4	11	9	7	5	7	6	5	5
24	6	4	5	4	6	4	8	9	4	3	5	4	4	3
25-29	13	10	12	9	11	10	15	10	12	8	12	8	11	6
30-34	7	6	7	6	8	5	6	3	9	6	7	4	9	5
Over 34	9	13	10	14	10	4	5	4	15	17	8	6	17	12
Total	100	100	100	100	100	100	100	100	100	100	100	100	100	100
Under 20	7	12	8	13	4	7	8	10	8	14	10	15	6	16
20-24	64	58	64	58	66	74	66	72	55	54	64	67	56	60
Over 24	29	30	28	30	29	19	25	18	36	32	26	18	37	24

	Education (O)		Engineering		Health		Law		Medicine		Science
Age in Years	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	0	0	0	0	0	0	0	0	0	0	0	0
18	0	0	0	0	0	0	0	0	0	0	1	1
19	0	0	2	4	7	12	2	4	1	2	11	15
20	4	6	18	25	18	21	6	9	1	2	23	28
21	4	7	27	33	14	13	17	18	11	13	22	22
22	5	7	19	18	9	7	21	22	31	32	12	11
23	5	7	11	7	6	5	15	12	25	25	7	6
24	4	6	6	4	4	3	6	7	13	11	5	4
25-29	20	18	11	6	16	12	12	11	12	10	10	8
30-34	17	11	4	1	9	10	7	7	3	3	5	3
Over 34	41	37	2	1	16	18	12	9	2	2	4	3
Total	100	100	100	100	100	100	100	100	100	100	100	100
Under 20	0	0	2	5	8	12	3	4	1	2	11	16
20-24	22	33	81	87	52	49	66	68	81	82	70	70
Over 24	78	66	17	8	41	39	32	27	17	15	19	14

The age profile of students vary a great deal by sex and also across fields of study. Except for the Education (O) field of study, in each field of study the proportion of females who are under 20 is higher than the proportion of males. For example, 43 percent of the females studying Education (I) are under 20 years old, but only 32 percent of the males studying Education (I) are in this age group. On the other hand in each field of study, except Medicine, the proportion of males who are 25 to 29 is higher than the proportion of females. In Medicine the proportion of males and females is the same in this age group.

Table 3.2 shows the age profile of students who completed courses in 1993. Most students tend to have finished the course when they were between 20 and 24 years of age. These data also have a pattern of variation which closely resembles that observed for the enrolment data.

The percentage breakdown by gender for students enrolled in 1994 is given in Table 3.3. For example, in Arts 25 percent of the under 18 age group is male and 75 percent is female. The aggregate figures indicate females outnumber males significantly in the younger age groups (20 years and younger) and the older age groups (30 years and over). However, this pattern is not uniform across all fields of study. Females seem to dominate across all age groups in Arts, Education (I) and Health, while male show dominance in Architecture and Engineering. In Science and Business males and females are in almost equal proportions in the younger age groups, but males are in higher proportions in the older age groups. Females are more numerous or equally as numerous as males in all age groups, except the over 24, in Law. In general, in Medicine there is a balance in the gender composition, except in the age groups between 21 and 29 when males are in higher numbers. Table 3.4 gives the gender balance in course completions. Once again, the pattern of variation is a reflection of that observed for the enrolment data..

Table 3.5 shows the variation in the number of years of enrolment (time in the system) for students by gender and field of study for 1994. For example, 31 percent of all female students in Architecture are in their first year of enrolment. On comparing with the data for 1993, which is not included in this report, 1994 data shows a significant increase in the percentage of both male and female students who are in the fourth or higher year of enrolment in Education (I), thus reflecting a reduced number commencing this field of study in 1994. Some differences between fields of study reflect the variation in the course length; for example, Engineering and Medicine are courses of longer duration, and thus, a higher proportion of students are in the fourth and fifth year of enrolment in these fields of study. Overall a female is less likely to be in her fourth or higher year of enrolment than a male is.

	All		Australian		Agriculture		Architecture		Arts		Business		Education (I)
Age in Years	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	41	59	41	59	52	48	57	43	25	75	49	51	18	82
18	43	57	43	57	57	43	60	40	28	72	52	48	20	80
19	44	56	43	57	61	39	61	39	29	71	51	49	22	78
20	45	55	45	55	61	39	63	37	31	69	53	47	24	76
21	49	51	49	51	63	37	64	36	35	65	56	44	27	73
22	51	49	51	49	69	31	62	38	36	64	57	43	32	68
23	52	48	51	49	69	31	66	34	39	61	58	42	31	69
24	52	48	50	50	66	34	63	37	39	61	60	40	34	66
25-29	50	50	49	51	69	31	74	26	38	62	59	41	33	67
30-34	46	54	45	55	67	33	73	27	33	67	60	40	31	69
Over 34	36	64	35	65	68	32	73	27	26	74	58	42	30	70
Total	45	55	45	55	63	37	64	36	31	69	55	45	26	74

	Education (O)		Engineering		Health		Law		Medicine		Science
Age in Years	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*	*Male*	*Female*
Under 18	11	89	83	17	16	84	43	57	52	48	49	51
18	16	84	85	15	17	83	41	59	50	50	52	48
19	18	82	84	16	17	83	47	53	50	50	54	46
20	12	88	84	16	19	81	46	54	52	48	55	45
21	15	85	86	14	21	79	47	53	54	46	58	42
22	15	85	89	11	24	76	51	49	53	47