What Does it Take? Access to Post‐Secondary Education for Student Parents 1 Trudy Smit Quosai Dr. Donna S. Lero Department of Family Relations and Applied Nutrition, University of Guelph, Guelph, ON There is no doubt that post‐secondary education (PSE) is an increasingly important component of human capital for individuals and for societies, and that it takes on even greater importance as a factor in economic competitiveness as Canada’s labour force shrinks. Education is closely linked to earning potential and is also a critical determinant of health and well‐being for individuals over the life course. For these reasons and others, the Government of Canada and provincial governments are carefully examining the factors that impede access to post‐secondary education and create barriers to students’ successful completion of a diploma or degree program. Several under‐represented or at‐risk groups have been identified to date, but one that has not received critical attention is student parents, typically older‐than‐
average students who combine parenting and post‐secondary education. This paper provides current information about student parents’ participation in post‐secondary education with a particular focus on financing PSE. There is evidence that single mothers are at a risk for accumulating higher levels of debt and experiencing more difficulty with debt‐loads; however, single mothers are often eligible for higher levels of government supports to assist with educational and other daily expenses. Background Literature Overall, the presence of children appears to be a disincentive for participating in and completing PSE for young parents (Lambert, Zeman, Allen & Bussière, 2004) and may affect completion rates for mothers who are returning to school after working (Zhang & Palameta, 2006). Single mothers, in particular, experience multiple challenges with respect to initiating and completing a post‐secondary diploma or degree. While recent years have seen economic improvements overall, single mothers are still over‐represented among low‐income families (Statistics Canada, 2007). This disadvantages single mothers and their children over the long term because individuals from low‐income families are less likely to participate in post‐
secondary education compared to those from higher income families (Junor & Usher, 2004). This relationship is particularly evident for those attending university where children from the highest income quartile are almost twice as likely as those from the lowest income quartile to We gratefully acknowledge funding received from HRSDC, Program Policy Division, Learning Branch for this research project. 1
1
attend, whereas college participation is more evenly spread across the income quartiles (Junor & Usher, 2004). Higher educational attainment typically translates into better job prospects and higher earnings; however, having some post‐secondary education without finishing a degree or diploma does not provide significant improvement over completing high school (Statistics Canada, 2004). This finding underlines the importance of not just participating in post‐
secondary education, but also of successfully completing a certificate, diploma, or degree program. While there is evidence that undergraduate student parents have high academic achievement (Holmes, 2005), adult students are also more likely to drop out of post‐secondary programs than traditional students (Grayson & Grayson, 2003). When students drop out of PSE, there is a loss of potential human capital and decreased ability to compete successfully for employment in an increasingly knowledge‐based economy. For student parents, there are additional costs to the family including financial sacrifice and forgone wages, lost time with partners and children, and increased stress and strain. Promoting the retention and success of student parents is important for promoting economic self‐sufficiency and for reducing family distress. Successful completion of PSE may be impacted by the ways in which student parents study. Student parents are more likely to participate on a part‐time basis, to interrupt their program, and to work more hours while studying (Holmes, 2005). Parents of children are also more likely to delay participating in PSE and this effect is most dramatic for women (Dubois, 2006). There is evidence that student parents incur higher education related debts (Allen & Vaillancourt, 2004; Holmes, 2005). Eligibility for the Canada Student Loans Program is impacted by the student’s (and their partner’s) income and assets. Financing a PSE program may require liquidating family assets such as cars, homes, and RRSPs, something that is not possible for most parents. Loans for part‐time students are also restrictive as they are limited to a $4000 cumulative maximum and require interest payments while the student is in school (CASA, undated). Students who live with a spouse or partner often have less access to government assistance and need to make greater use of private savings, loans or employment income (EKOS, 2003). Student parent participation is also influenced by changing social policies such as those which limit access to social assistance while studying and policies affecting access to child care (Butterwick & White, 2006; Reed, 2005). Methods It is challenging to find a single data source to provide comprehensive information about post‐secondary students. The information in this paper derives from analyses of four national data sets (the Labour Force Survey 2 , the Survey of Labour and Income Dynamics 3 , the 2
The Labour Force Survey (LFS) 1976‐2005. 2
National Graduates Survey 4 , and the Youth In Transition Survey 5 ) which, together, provided information about student parents’ participation in PSE. Descriptive statistics were used to create a profile of student parents in Canada and to describe participation and borrowing trends. Logistic regression models using YITS and SLID data were developed to predict participation and retention in PSE. Each of the data sets provided information about a different aspect of the student parent population. Analysis of LFS data enabled examination of trends in student parent participation over time (age 16‐59), SLID data provided descriptive information about student parents aged 16‐59, YITS data provided descriptive information about young parents and student parents (age 22‐24), and NGS data described characteristics of student parent graduates. Profile of Student Parents Levels of Participation Student parents comprise a sizable minority of all students studying at post‐secondary institutions. LFS data suggest the percentage of student parents as a proportion of all PSE students increased between 1976 and 1991 when it reached a peak of 16% and has since fallen to its current 2005 level of 12% or approximately 153,000 student parents (LFS, 1976‐2005). The SLID 2004 data shows slightly higher proportions of student parents 6 at 14% of all PSE students: 17% of college students, and 11% of university students (SLID, 2004). The numbers are similar for college and university graduates where student parents accounted for approximately 15% of PSE graduates, 20% of college graduates, and 12% of university graduates (NGS, 2002). Attending College vs. University For those who attend post‐secondary programs, there is evidence that college programs are more accessible to student parents based on reported levels of participation. When student parents study, they are more likely to attend a college than a university (Table 1). Additionally, much of the growth in student parent participation in PSE has occurred in the college sector. Student parent participation in college programs grew by 132% between 1976 The Survey of Labour and Income Dynamics (SLID) 2004 Cross‐sectional data (panels 3 and 4 overlap) and Panels 3 and 4 longitudinal data (1999‐2004). 4 The National Graduates Survey (NGS) Class of 2000 (2002) and 1990, 1995, 2000 graduating class data (1992, 1997, 2002). 5 The Youth in Transition Survey (YITS) Cross‐sectional data Cycle 3 (2003) (18‐20 year old cohort born 1979‐1981) and Cycles 1‐3 longitudinal data (1999 ‐ 2003) (18‐20 year old cohort born 1979‐1981). 6 It is likely that this discrepancy results from the relatively smaller sample within SLID and differences in how enrolment in PSE programs is reported. 3
3
and 2005. During the same period, student parent participation in universities increased at a substantially slower rate (55% growth). Female student parents appear to drive the observed increase in college student parent participation (LFS, 1976‐2005). Table 1: College and University Participation of Parents and Non‐Parents Age 16-59
(SLID, 2004)
Age 22-24
(YITS, 2003)
Parents
Non-Parents
% attending
university
38%
51%
% attending
college
62%
49%
Total attending
PSE
100%
100%
% attending
university
32%
58%
% attending
college
68%
42%
Total attending
PSE
100%
100%
Characteristics of Student Parents Although student parents comprise a heterogeneous group, a general profile of student parents can be described. Overall, student parents tend to be female, older than average students, and are more likely to be partnered than traditional younger students. Female students have accounted for much of the growth in the percentage of student parents attending PSE programs in the last three decades. Female student parents have grown from 7% of all post‐secondary students in 1976 to 9% in 2005. During the same period, male student parent participation has dwindled from 6% of all PSE students in 1976 to 4% in 2005 (LFS 1976‐
2005). In 2004 women comprised 55.3% of students without children, but 73.4% of student parents. Female student parents in university were slightly more likely to be married or partnered than those in college (73% vs. 67%) (SLID, 2004). While 87% of childless students were between the ages of 16‐29, only 21% of parents fell into this age group. Two thirds of student parents fell between the ages of 30 and 44 years and one in eight was 45 years or older (SLID, 2004). 4
Part‐Time Study Although more student parents are studying full‐time than in previous decades, a higher proportion of student parents still study on a part‐time basis compared to non‐parents. This is particularly notable among university students where only 21% of non‐parents studied part‐time compared to 52% of parents (SLID, 2004 7 ). However, among student parents, single mothers appear to be more likely to study on a full‐time basis (82% according to NGS, 2002 8 ) than fathers (64%) or partnered mothers (56%). This may reflect a need for single mothers to study full‐time in order to qualify for financial support along with a disincentive to combine employment and study because earnings may reduce government loan amounts. It may also reflect the difficulty of balancing part‐time study, employment, and family life for single mothers. There were also gender related differences in the reasons parents gave for studying on a part‐time basis and for taking a leave of from studying. Mothers were more likely than fathers to cite child care or family responsibilities; fathers were more likely to report employment reasons for not studying on a full‐time basis or for taking a leave from their studies (NGS, 2002). Balancing School, Family, and Employment Many students combine study with employment and student parents are no exception. In 2004, parents and non‐parents enrolled in PSE programs were equally likely to be employed (just over 80%); however, when employed, student parents were more likely to be employed for the full year compared to non‐parents (55% and 41%, respectively) (SLID, 2004). As well, student parents were more likely to be the major earner in the family, especially among university students where 55% of student parents were the major earner compared to 32% of non‐parents (SLID, 2004). There is evidence that young fathers maintain a stronger attachment to the labour force while studying than do young mothers, particularly single mothers. Fathers under 25 years of age were employed for 63% of the months they were enrolled in full‐
time study compared to 36% among single mothers (YITS, 2003). Financing Studies in PSE Overall, there is evidence that parents make extensive use of government student loans to finance their education. Again, there are gender differences in funding patterns; fathers are more likely to use personal earnings to pay for school, while mothers, and single (never married) mothers in particular, are more likely to borrow money for education expenses. This suggests that mothers and single mothers in particular, may sacrifice financial security by foregoing employment and accumulating debt in order to balance education and family needs. Much of the information related to borrowing comes from the National Graduates Survey. 7
According to SLID, full‐time is determined to be more than 15 hours per week for those in college and more than 12 hours per week for those in university.
8
According to NGS, full‐time, part‐time or combined full and part‐time are as reported by the respondent.
5
Between 1990 and 2000 the proportion of students without children who received government loans remained at approximately 50%; over the same period the percentage of parents receiving loans increased from 42% to 51%. Of note is the observation that the proportion of single female student parents who received government loans increased from 69% to 80% over that period (NGS, 1992, 1997, 2002). Student‐parent graduates, however were more likely than non‐parents to report having difficulties repaying loans in the two years following graduation. Less than one‐quarter of non‐parents reported difficulty compared to approximately one‐third of parents (Table 2). Single female student parents were the most likely to report difficulties with repayment, with the proportion increasing from 31% in 1990 to 46% in 2000 (NGS, 1992, 1997, 2002). There is evidence that single mothers are struggling with loan repayment. Single mothers were the most likely to have difficulty repaying government loans (46%), to receive assistance with paying loans (40%), and, likely because of high debt loads, received debt reduction on government loans (56%) (NGS, 2002). Patterns of borrowing and repayment differ by type of institution and by gender. Parents who graduated college in 2000 borrowed more than their non‐parent peers and reported only slightly higher earnings two years after graduation. Consequently, median debt‐to‐earnings ratios 9 were higher for parents than non‐parents (0.47 and 0.32, respectively). Mothers who graduated college had the highest mean levels of debt at graduation ($15,900) and the highest debt‐to‐earnings ratio (median 0.55 / mean 0.73) (NGS, 2002). See Table 3. There was little difference in mean government loan amounts for parents and non‐parents who graduated from university ($17,900 and $17,400, respectively). Fathers had the highest earnings of any graduates ($50,800) which resulted in a relatively lower debt‐to‐earnings ratio (mean 0.43 / median 0.24). Mothers who graduated university borrowed the highest amount of all graduates ($18,400) and had the highest median debt‐to‐earnings ratio of university graduates (mean 0.66 / median 0.48) (NGS, 2002). See Table 4. 9
[Total student loans at graduation ($2002) / annual income reported at two years after graduation ($2002)] for those who have positive values for loans and for income. 6
Table 2: Information on Borrowing among Graduates by Gender and Marital Status Single
Female
Married
Female
Parents
Divorced
Female
Received Government Loan
80.2%
44.6%
Borrowed From Other Sources
15.8%
15.9%
Bursaries/Grants
48.3%
24.3%
Scholarships
21.0%
19.6%
Debt Reduction
55.6%
37.1%
Difficulty Paying Loan
45.8%
28.2%
Assistance with Repaying
39.9%
25.4%
Default on Loan
27.1%
18.2%
Applied for Loan (of those who did not receive loans)
64.1%
25.5%
39.9%
22.1%
49.8%
40.7%
36.9%
22.4%
Total
Females
53.5%
17.6%
30.7%
20.2%
44.7%
34.7%
32.4%
21.9%
5.9%
Males
Single
Female
46.2%
20.0%
21.3%
16.8%
30.3%
35.4%
25.0%
38.4%
5.8%
Married
Female
50.1%
21.5%
22.0%
32.0%
25.7%
21.6%
29.8%
19.0%
Non-Parents
Divorced
Total
Female
Females
50.6%
19.0%
17.5%
26.6%
24.2%
20.9%
24.4%
12.5%
32.8%
15.4%
21.4%
18.0%
18.5%
38.2%
15.0%
42.5%
Males
49.8%
20.6%
20.6%
30.1%
25.1%
21.7%
27.7%
17.7%
10.3%
48.2%
23.9%
18.1%
28.0%
24.5%
22.1%
30.0%
22.7%
9.9%
Data as reported by respondents. Source: NGS 2002. Table 3: Loans and Earnings for College Graduates Non-Parents
Student
Loan Owed
at Grad in
2000 (in
2002 $)
Male
Female
Total
Mean
Median
Mean
Median
Mean
Median
$11,000
$8,400
$11,000
$8,400
$11,000
$8,400
Student
Loan
Owed in
2002
$8,500
$6,000
$8,700
$7,000
$8,600
$6,500
Amount
Owed to
All Other
Sources
$3,900
$1,000
$4,200
$1,500
$4,100
$1,100
Annual
Earnings
$2002
$34,200
$32,900
$27,500
$26,000
$30,400
$29,100
Parents
Proportion
of Debt
paid off in
2002
Debt-toEarnings
Ratio
2002*
Student
Loan Owed
at Grad in
2000 (in
2002 $)
0.43
0.30
0.39
0.27
0.41
0.29
0.41
0.27
0.49
0.36
0.46
0.32
$15,200
$10,500
$15,900
$14,700
$15,700
$13,700
Student
Loan
Owed in
2002
$12,900
$10,000
$12,900
$11,000
$12,900
$11,000
Amount
Owed to
All Other
Sources
$5,400
$2,000
$2,600
$0
$3,600
$0
Annual
Earnings
$2002
$40,100
$37,600
$29,700
$26,400
$33,200
$30,600
Proportion
of Debt
paid off in
2002
Debt-toEarnings
Ratio
2002
0.36
0.29
0.34
0.27
0.34
0.25
0.42
0.30
0.73
0.55
0.65
0.47
* [Total student loans at graduation ($2002) / annual income reported at two years after graduation ($2002)] for those who have positive values for loans and for income. values rounded to nearest 100. Source: NGS 2002. 7
Table 4: Loans and Earnings for University Graduates Non-Parents
Student Loan
Owed at
Grad in 2000
(in 2002 $)
Male
Female
Total
Mean
Median
Mean
Median
Mean
Median
$18,000
$15,800
$17,000
$14,700
$17,400
$15,800
Student
Loan
Owed in
2002
$14,000
$11,200
$14,500
$12,000
$14,300
$12,000
Amount
Owed to All
Other
Sources
$7,000
$2,000
$7,000
$2,600
$7,000
$2,000
Annual
earnings
2002
$40,400
$38,500
$34,800
$34,900
$36,900
$35,900
Parents
Proportion
of debt
paid off in
2002
Debt-toearnings
Ratio
2002
0.40
0.26
0.41
0.27
0.40
0.26
0.66
0.41
0.69
0.46
0.68
0.44
Student
Loan Owed
at Grad in
2000 (in
2002 $)
$17,000
$11,600
$18,400
$15,800
$17,900
$14,700
Student
Loan
Owed in
2002
$17,100
$14,500
$17,200
$14,000
$17,100
$14,000
Amount
Owed to All
Other
Sources
$8,200
$0
$7,000
$2,000
$7,500
$1,000
Annual
earnings
2002
$50,800
$48,000
$39,100
$37,500
$43,100
$41,000
Proportion
of debt
paid off in
2002
Debt-toearnings
Ratio
2002
0.37
0.25
0.33
0.17
0.35
0.19
0.43
0.24
0.66
0.48
0.58
0.39
* [Total student loans at graduation ($2002) / annual income reported at two years after graduation ($2002)] for those who have positive values for loans and for income. values rounded to nearest 100. Source: NGS 2002. 8
Models to Predict Participation and Completion of PSE A series of Logistic Regression models were generated which provide preliminary information about factors that predict participation in and completion of PSE. The models were created using a six‐year panel of longitudinal SLID data from 1999 – 2004 for respondents aged 16‐59 and using YITS Cycles 1, 2, and 3 (1999‐2003) for traditional‐aged students (22‐24). It is important to note that the models are preliminary. It is possible that important variables were not included either through our own omission or because the information was not clearly derivable from the data. Additionally, model significance may have been impacted by small sample size when exploring outcomes for student parents. Logistic Regression was used to predict various discrete outcomes: starting college vs. not starting, starting university vs. not starting, graduating from college vs. withdrawing, and graduating from university vs. withdrawing (for ages 16‐59, SLID) and attending PSE (college or university) vs. not attending, attending university vs. college, and leaving PSE (college or university) without graduating vs. not leaving (graduating or continuing) (for ages 22‐24, YITS). Only key points of the more robust models are described here; relevant results are shown in Tables 5 to 9. The models were generated using direct Logistic Regression (as if variables were entered simultaneously). Thus, the contribution of each predictor is considered to be over and above the contributions made by all other predictors. The statistical significance of the predictors was tested using the Wald test [equal to the squared coefficient (B) divided by its standard error squared] (Tabachnick & Fidell, 2007). We used a 95% confidence interval (p<0.05). The relative strength of the predictors was assessed by comparing the odds ratios, the change in the odds of a specific outcome for one unit increase in the predictor. Model Summaries Overall, parents are less likely to attend PSE compared to non‐parents (0.8 times as likely to start college, 0.7 times as likely to start university according to SLID and 0.3 times as likely to attend PSE in the YITS sample)(Table 5, Table 7). As well, among traditional‐aged students who attended PSE, parents are 0.3 times as likely to attend university compared to college (see Table 8). Similar factors predicted participation in PSE for parents and non‐
parents. These factors included gender (being female), being single, having better high school grades, and having parents who had obtained a university degree. Having one or more children also limits successfully completing PSE. The YITS model suggests that young parents (< 25 years of age) were 1.7 times more likely to leave PSE without a degree or diploma than non‐parents (Table 9). The SLID models based on students age 16‐59 also show an odds ratio less than 1 for parents completing college (0.948) and university (0.729); however these were not statistically significant predictors (Table 6). There were differences among the models in terms of relative strength of prediction for completion of PSE. 9
For young parents (age 22‐24) being female, single, and childless reduced the likelihood of leaving PSE before obtaining a diploma or degree. Among students age 16‐59 (SLID data) gender, marital status, and parental status were not significant predictors. These discrepancies may derive from differences in the predicted outcome (withdrawing vs. graduating for SLID and leaving vs. continuing for YITS) or from differences in sample size. It is also possible that the models reflect, among other things, differences in modes of study (e.g. older students sometimes do not intend to complete a PSE program; rather they may be upgrading specific skills). Individual models were run for parents and non‐parents; however because of small sample sizes the actual predictive power of these models was very low. While the disaggregated models are not predictively robust, they do suggest some interesting areas for further exploration, for example how gender and marital status jointly affect educational decisions and how financial resources and one’s relative role in contributing to family income affects PSE participation. There are important limitations to the models described. Although significant results were produced, the actual predictive value of some models was poor, particularly for PSE completion and separate parent and non‐parent models. As well it is not possible to determine the direction of effect; for example, not participating in PSE may leave people more free to enter into a relationship, rather than partner status affecting PSE participation. Some factors that may have been important predictors could not be included because of limitations in the data set (e.g., full and part‐time status was only available starting in 2002 for SLID). There are also potentially relevant factors that were not available for inclusion. Factors which might be important predictors of PSE participation and completion include individuals’ accumulated savings for expenses like PSE, measures of aptitude including previous academic achievement, measures of aspirations for higher education, and measures of support from other family members to participate in PSE. A longitudinal survey of learners that addresses the wide range of factors affecting participation and persistence would likely provide far superior models. 10
Table 5: Summary of Logistic Regression Models to Predict Starting College or University (Age 16‐59) Start College
Variable
Contrast
B
Parent of a Child
No (0) vs. Yes (1)
Age
Continuous (per year)
Census Family Income /
√family size
Ratio of Individual Income
to Family Income
Start University
0.093
Odds
Ratio
0.677
**
0.004
0.900
**
0.014
0.001
1.014
**
**
-0.47
0.082
0.625
**
**
Stnd Err.
Odds Ratio
B
-0.204
0.062
0.816
**
-0.389
-0.075
0.003
0.928
**
-0.105
Continuous (per $1,000)
-0.004
0.001
0.996
**
Continuous (0-1)
-0.602
0.075
0.548
Stnd Err.
Gender
Male (0) vs. Female (1)
0.115
0.048
1.122
0.452
0.061
1.571
**
Immigrant Status
No (0) vs. Yes (1)
0.129
0.069
1.137
0.364
0.09
1.439
**
Parental Educational
Attainment
No PSE Certificate (0) vs.
Obtained PSE Certificate (1)
No Partner (0) vs. Partnered
(1)
0.042
0.052
1.043
0.196
0.063
1.216
**
-0.475
0.069
0.622
**
-0.672
0.094
0.511
**
B.C. (0) vs. Maritimes (1)
-0.216
0.104
0.806
**
0.058
0.131
1.06
B.C. (0) vs. Quebec (1)
-0.317
0.081
0.728
**
-0.147
0.101
0.864
B.C. (0) vs. Ontario (1)
0.121
0.072
1.129
-0.121
0.091
0.886
Marital Status
Region of Residence
B.C. (0) vs. Man./Sask. (1)
-0.319
0.112
0.727
**
0.015
0.137
1.015
B.C. (0) vs. Alberta (1)
0.193
0.091
1.213
**
-0.394
0.124
0.674
Employed All Year (0) vs.
Employed Part Year (1)
0.195
0.058
1.215
**
0.066
0.071
1.068
Employed All Year (0) vs.
Unemployed (1)
0.307
0.072
1.359
**
0.249
0.092
1.283
**
No (0) vs. Yes (1)
0.114
0.092
1.121
-0.479
0.138
0.620
**
**
Labour Force Status
Census Family in Receipt
of Social Assistance
Model chi square
Χ² (df=16, N=10,676) = 2875.56, p<0.001
2
Effect Size
10
Nagelkerke R =0.28 ; The model predicts
41% of those who start college and 95% of
those who do not start college.
Χ² (df=16, N=10,765) = 3355.21, p<0.001
2
Nagelkerke R =0.38; The model predicts
39% of those who start university and 98%
of those who do not start university.
** Significant at p<0.05
Source: SLID 1999-2004
The Nagelkerke measure approximates a value similar to the R2 in regression. 10
11
Table 6: Summary of Logistic Regression Models to Predict Completing College and University (Age 16‐59) Graduated College (combined)
Contrast
Parent of a Child
No (0) vs. Yes (1)
-0.053
0.172
0.948
-0.317
0.235
0.729
Age
Continuous (per year)
-0.005
0.009
0.995
-0.036
0.012
0.965
Continuous (per $1,000)
-0.006
0.004
0.994
0.005
0.003
1.005
Continuous (0-1)
0.567
0.205
1.763
0.185
0.241
1.203
Male (0) vs. Female (1)
0.051
0.118
1.053
-0.182
0.143
0.834
-0.573
0.171
0.564
-0.137
0.205
0.872
0.003
0.121
1.003
0.195
0.149
1.216
-0.062
0.175
0.94
-0.283
0.231
0.754
Census Family Income /
√family size
Ratio of Individual
Income to Family
Income
Gender
Immigrant Status
No (0) vs. Yes (1)
Parental Educational
Attainment
No PSE Certificate (0) vs.
Obtained PSE Certificate (1)
Marital Status
No Partner (0) vs. Partnered (1)
B
Stnd Err.
Graduated University (combined)
Variable
Odds Ratio
B
**
**
Stnd Err.
Odds Ratio
**
Region of Residence
B.C. (0) vs. Maritimes (1)
Labour Force Status
Census Family in
Receipt of Social
Assistance
Model chi square
Effect Size
0.048
0.319
1.049
0.617
0.314
1.853
**
B.C. (0) vs. Quebec (1)
-0.372
0.206
0.689
1.225
0.252
3.405
**
B.C. (0) vs. Ontario (1)
0.052
0.188
1.054
0.642
0.23
1.901
**
B.C. (0) vs. Man./Sask. (1)
-0.53
0.298
0.589
0.656
0.302
1.927
**
**
B.C. (0) vs. Alberta (1)
-0.26
0.245
0.771
1.462
0.339
4.313
Employed All Year (0) vs.
Employed Part Year (1)
0.026
0.131
1.026
-0.037
0.166
0.963
Employed All Year (0) vs.
Unemployed (1)
-0.204
0.184
0.815
-0.332
0.206
0.717
No (0) vs. Yes (1)
-0.628
0.207
0.534
-1.018
0.387
0.361
**
Χ² (df=16, N=837) = 54.72, p<0.001
2
Nagelkerke R =0.05; The model predicts
23% of those who withdrew from college
and 86% of those who graduated from
college.
**
Χ² (df=16, N=599) = 99.84, p<0.001
2
Nagelkerke R =0.13; The model predicts
32% of those who withdrew from
university and 91% of those who
graduated university.
** Significant at p<0.05
Source: SLID 1999-2004
12
Table 7: Summary of the Logistic Regression Models to Predict Attending PSE among Young Adults Age 22‐24 Attend PSE
Variable
Contrast
Parent of a Child
No (0) vs. Yes (1)
Urban or Rural
Rural (0) vs. Urban (1)
Gender
Female (0) vs. Male (1)
No Partner (0) vs. Partnered
(1)
Marital Status
B
Stnd
Err.
Odds Ratio
-1.153
0.074
0.316
**
0.319
0.058
1.375
**
-0.605
0.052
0.546
**
-0.246
0.062
0.782
**
Region of Residence
Parental Educational
Attainment
Average High School
Grade 11
Hours / Week
Worked in High
School 12
B.C. (0) vs. Maritimes (1)
0.474
0.115
1.606
**
B.C. (0) vs. Quebec (1)
0.392
0.085
1.479
**
**
B.C. (0) vs. Ontario (1)
0.532
0.08
1.703
B.C. (0) vs. Man. / Sask. (1)
0.009
0.11
1.009
B.C. (0) vs. Alberta (1)
0.096
0.097
1.101
-1.021
0.078
0.360
**
0.889
0.03
2.432
**
-0.082
0.019
0.922
**
Obtained University Degree
(0) vs. No University Degree
(1)
Continuous (per level
increase)
Continuous (per level
increase)
Model Chi Square
Х2 (df=12, N=12,340) = 2391.62, p<0.001
Effect Size
Nagelkerke R =0.27; The model predicts
95% of those who attended PSE and
27% of those who did not attend PSE.
2
** Significant at p<0.05 Source: YITS Cycles 1-3
Odds per level of increase in average high school grade reported in 10 mark intervals, 1=under 50%, 2=50‐59, 3=60‐69, 4=70‐79, 5=80‐89, 6=90‐100. Odds per level of increase in reported average hours of paid work in a week during last year of high school, 0=0,did not work, 1=1‐9, 2=10 – 19, 3=20 ‐ 29, 4=30 or more hours. 11
12
13
Table 8: Summary of the Logistic Regression Models to Predict Attending University vs. College among Young Adults Age 22‐24 Attend University
Variable
Contrast
Parent of a Child
No (0) vs. Yes (1)
Urban or Rural
Gender
Marital Status
B
Stnd
Err.
Odds Ratio
-1.184
0.103
0.306
**
Rural (0) vs. Urban (1)
0.616
0.063
1.851
**
Female (0) vs. Male (1)
No Partner (0) vs. Partnered
(1)
-0.23
0.05
0.794
**
-0.541
0.063
0.582
**
Region of Residence
0.451
0.121
1.569
**
B.C. (0) vs. Quebec (1)
B.C. (0) vs. Maritimes (1)
-0.898
0.088
0.407
**
B.C. (0) vs. Ontario (1)
-0.288
0.083
0.75
**
B.C. (0) vs. Man. / Sask. (1)
0.627
0.129
1.873
**
B.C. (0) vs. Alberta (1)
0.379
0.112
1.461
**
-0.911
0.059
0.402
**
1.156
0.034
3.176
**
-0.211
0.02
0.810
**
Parental Educational
Attainment
Obtained University Degree
(0) vs. No University Degree
(1)
Average High School
Grade 13
Hours / Week Worked in
High School 14
Continuous (per level
increase)
Continuous (per level
increase)
Model Chi Square
Χ² (df=12, N=9,720) = 2816.30, p<0.001
Effect Size
Nagelkerke R =0.35; The model
predicts 63% of those who enrolled in
college programs and 80% of those who
enrolled in university programs.
2
** Significant at p<0.05
Source: YITS Cycles 1-3
Odds per level of increase in high school grade reported in 10 mark intervals, 1=under 50%, 2=50‐59, 3=60‐69, 4=70‐79, 5=80‐89, 6=90‐100. Odds per level of increase in reported average hours of paid work in a week during last year of high school, 0=0,did not work, 1=1‐9, 2=10 to 19, 3=20 29, 4=30 or more hours. 13
14
14
Table 9: Summary of the Logistic Regression Models to Predict Leaving Post‐Secondary Education among Young Adults Age 22‐24 Leave PSE
0.512
Stnd
Err
0.171
Rural (0) vs. Urban (1)
-0.038
0.096
0.962
Gender
Female (0) vs. Male (1)
0.456
0.077
1.578
**
Marital Status
No Partner (0) vs.
Partnered (1)
0.455
0.106
1.576
**
0.212
0.092
1.236
**
-0.453
0.048
0.636
**
Continuous (0-1)
0.002
0.001
1.002
No (0) vs. Yes (1)
0.000
0.085
1.000
No (0) vs. Yes (1)
-0.139
0.079
0.871
No (0) vs. Yes (1)
0.432
0.217
1.540
No (0) vs. Yes (1)
-0.066
0.107
0.936
0.157
0.081
1.170
Variable
Contrast
Student Parent
No (0) vs. Yes (1)
Rural or Urban
Parental Educational
Attainment
Average High School
Grade 15
Proportion of Months in PSE
Also Working
Moved Before End of PSE
Ever Received Government
Student Loan
Ever Received Social
Assistance
Ever Studied by Distance
Ever Studied Part-Time
Highest level of Study
16
Average Income
Average Income from
Spouse 17
Model Chi Square
Obtained University
Degree vs. No University
Degree
Continuous (per level
increase)
B
Odds Ratio
1.668
**
**
No (0) vs. Yes (1)
College (0) vs. University
(1)
Continuous (per $1000)
-0.617
0.083
0.539
**
-0.033
0.006
0.968
**
Continuous (per $1000)
-0.029
0.007
0.971
**
Χ² (df=15, N=8,127) = 424.62, p<0.001
2
Effect Size
Nagelkerke R =0.10; The model
predicts 0.2% of those who left PSE
and 100% of those who did not
leave PSE.
** Significant at p<0.05
Source: YITS Cycles 1-3. Odds per level of increase in high school grade reported in 10 mark intervals, 1=under 50%, 2=50‐59, 3=60‐69, 4=70‐79, 5=80‐89, 6=90‐100. Odds per $1,000 based on average annual income until last year of PSE. 17 Odds per $1,000 based on average annual income until last year of PSE. 15
16
15
Implications and Policy Recommendations There is evidence that student parents comprise a sizable minority of PSE students (one in seven in 2004). With increasing demands for life‐long learning, increased immigration, and increased technological changes we can expect that greater numbers of parents will participate in post‐secondary education. There are many benefits of enhancing access to post‐secondary education for student parents. Learning through the life span enhances employment and career opportunities, social and personal well‐being, and community engagement. Beyond the broader social and economic benefits of post‐secondary education, parents serve as important role models for their children. Children are more likely to attend PSE themselves if their parents have attended and if discussion of post‐secondary education occurs in the home (Berger, Motte, & Parkin, 2007). In spite of the challenges of balancing school and family, children are likely to benefit from the environment of learning that student parents can create in their homes. So what does it take to facilitate success for student parents? Student parents face numerous challenges related to balancing study with family demands and often with work demands as well. Policy suggestions relate to facilitating access and program completion for this group of predominantly adult learners. Student parents simply do not fit into the current student finance model. This challenge is particularly evident with respect to accessing government financial assistance particularly for partnered parents. Their life situations and study patterns often make them ineligible for government student loans and loan amounts may not be sufficient to meet family needs as well as educational expenses. Alternatively, students such as single mothers may be dependent on loans for their livelihood with requirements to study full‐time in order to meet the eligibility criteria. Often, these mothers risk incurring a disproportionate amount of debt which can impact their ability to become self‐
sufficient following graduation. Public policies and institutional practices should be reviewed to remove impediments to student parents’ access to, and participation in PSE programs, facilitate their success through program completion, and reduce the likelihood of burdensome, high levels of education‐related debt. Like all students, student parents would benefit from affordable tuition. They also require financial support programs that meet the needs of non‐traditional students including loan and bursary programs for part‐time student as well as enhanced supports through the tax system. Additionally, supports such as affordable, accessible child care and after‐school programs, and social assistance policies that facilitate adult learning by allowing students to maintain benefits while learning will encourage participation and successful completion of post‐secondary programs by parents. It is also important to recognize that student parents comprise a heterogeneous group. The life situation and therefore the needs of young parents may be quite different from those of older parents. Supports that are responsive to diverse needs will go further to enhance access and success for student parents. 16
In today’s knowledge‐based economy, post‐secondary education is an important element for successful employment. As well, adults need to continue to learn throughout their lives to adapt to changes and new skill demands in the workplace. Access to PSE for Canadians with diverse backgrounds and with greater or lesser financial resources is an important focus for policy analysis. Students who have children are one group who face unique challenges in accessing and completing PSE. These challenges include access to sufficient funding, balancing competing time demands, being sensitive and responsive to children’s needs, and negotiating the transition into PSE from non‐traditional streams. Appropriate supports from government and from institutions are needed to facilitate learning throughout the lifespan. For further information please contact Trudy Smit Quosai, Family Relations and Applied Nutrition, University of Guelph, Guelph, ON N1G 2W1(trudys@uoguelph.ca).
17
References Allen, M. & Vaillancourt, C. (2004). Class of 2000: Profile of postsecondary graduates and student debt. Ottawa, ON: Culture, Tourism and the Centre for Education Statistics Division. Berger, J., Motte, A. & Parkin, A. (2007). The price of knowledge: Barriers to post‐secondary education. Retrieved September 9, 2007, from http://www.millenniumscholarships.ca/images/Publications/POK07‐ch2_e.p Butterick, S. & White, C. (2006). A path out of poverty: Helping BC income assistance recipients upgrade their education. Vancouver, BC: Canadian Centre for Policy Alternatives. CASA. (undated). Report on first year student, international student, and non‐traditional student focus groups. Ottawa: ON: Canadian Alliance of Student Associations. Dubois, J. (2006). Trends in student borrowing and pathways: Evidences from the 1990, 1995 and 2000 classes. Gatineau, QC: Human Resources Skills and Development Canada. EKOS Research Associates. (2003). Making ends meet: The 2001‐2002 student financial survey. Montreal, QC: Canada Millennium Scholarship Foundation. Grayson, J. Paul & Grayson, K. (2003). Research on retention and attrition. Montreal, QC: Canada Millennium Scholarship Foundation. Holmes, D. (2005). Embracing differences: Post‐secondary education among Aboriginal students, students with children, and students with disabilities. Montreal, QC: Canada Millennium Scholarship Foundation. Junor, S., & Usher, A. (2004). The price of knowledge 2004: Access and student finance in Canada. Montreal, QC: Canadian Millennium Scholarship Foundation. Lambert, M., Zeman, K, Allen, M. & Bussière, P. (2004). Who pursues postsecondary education, who leaves and why: Results from the Youth in Transition Survey. Ottawa, ON: Statistics Canada, Culture, Tourism and the Centre for Education Statistics. Lero, D.S., Smit Quosai, T. (2006). Access to post‐secondary education for student parents: A review of data sources Submitted to HRSDC. Reed, K. (2005). Fairness in education for single parents in Nova Scotia. Halifax, NS: Canadian Centre for Policy Alternatives. 18
Statistics Canada (2004). Canadian Labour Market at a Glance. Retrieved June 1, 2005 from http://www.statcan.ca/english/freepub/71‐222‐XIE/2004000/chart‐f37.htm Statistics Canada. (2007). Persons in low income after tax, by prevalence in percent (2001 to 2005). CANSIM. Retrieved October 30, 2007 from http://www40.statcan.ca/l01/cst01/famil19a.htm Tabachnick, B. & Fidell, L. (2007). Using multivariate statistics 5th edition. Boston: Pearson/Allyn & Bacon. Zhang, X. & Palameta, (2006). Participation in adult schooling and its earnings impact in Canada. Ottawa. ON: Statistics Canada, Catalogue no.11F0019MIE 19