ECONOMIC ANALYSIS OF DEMAND
FOR DISTANCE EDUCATION IN
CANADA
BY
Edward Hans Kofi Acquah, PhD.
Senior Institutional Analyst & Academic Expert
ATHABASCA UNIVERSITY
CANADA
AIR 2009 ANNUAL FORUM
MAY 30-JUNE 03, 2009
ATLANTA, GA. USA.
INTRODUCTION AND BACKGROUND
Distance Education
• Has come of age
• Become a significant part of postsecondary education
Definitions:
1. “a formal educational process in which the students and the
instructor are not in the same place” (Prasad & Lewis, 2008)
Definition implies that instruction may be:
• Synchronous: real time or simultaneous
• Asynchronous: not real time or simultaneous
And may involve:
INTRODUCTION AND BACKGROUND (continued)
• Communications: through the use of video, audio or by computer and
internet technologies, or
• Communications: by written correspondence and use of technologies, e.g.
CD-ROM
2.
“An educational practice promoting a learning process that brings
knowledge closer to the learner” (Deschenes & et all, 1996)
3. Distance Education Courses and Programs
Are classified as ff:
• Online Courses/Programs: all instruction is online;
• Hybrid/Blended Online Courses/Programs: combines online and in-class
instructions with a reduced in-class seat time for students;
• Other Distance Education Courses/Programs: postal correspondence
RESEARCH OBJECTIVES
• To analyze the demand for distance education in Canada
• To determine which factors influence this demand
• To determine gender differences in terms of the factors that influence
the demand
• To determine the policy implications for Canadian universities and
colleges offering distance/online education
HISTORICAL PERSPECTIVES
• DE has experienced growth and expansion in North America in recent
years in terms of:
• DEMAND: program enrolments and course registrations;
• SUPPLY: Institutions, learning management systems (LMS), delivery
modes, faculty, and innovative learning resources;
• In the fall of 2006, 3.5 million students (19.8% of PSE enrolments) in
the US took at least one course online;
• The Table below gives a better perspective on growth in DE (Babson
Survey Research Group & Sloan Foundation, 2008):
GROWTH IN THE DEMAND FOR DE IN THE US
ENROLMENT DEMAND
Period
Fall 2002
Fall 2006
2006 Increase
#
#
#
Doctoral/Research
258,489
566,725
308,236
Compound
Annual
Growth
Rate%
21.7
Master’s
Baccalaureate
Community
Colleges
Specialized
Programs
335,703
130,677
806,391
686,337
170,754
1,904,206
350,634
40,077
1,097,905
19.6
6.9
24.0
71,710
160,268
88,558
22.3
PROGRAMS
HISTORICAL PERSPECTIVES (CONTD)
• Parsad & Lewis (2008) have shown that 66% of 1,600 Title IV Degreegranting PSE institutions offered Online, Hybrid/Blended Online or
other Distance Education courses in the 2006-07 academic year.
These are the pioneers of DE in Canada:
•
•
•
•
•
•
•
The Queen’s University, 1889
University of Saskatchewan, 1907
Xavier University, 1935
The University of British Columbia, 1950
Memorial University of Newfoundland, 1967
University of Waterloo, 1968
Ryerson Polytechnic University, 1970
PIONEERS OF DE IN CANADA
•
•
•
•
•
Simon Fraser University, 1975
University of Victoria, 1979
British Columbia Institute of Technology, 1985
McGill University, 1987
Salt College of Applied Arts & Technology, 1988
• Athabasca University, Canada’s Open University (1973) &
• Tele University & Open Learning Institute (1975) were fashioned on
the British Open University (1971) model
• In 1994, there were 200,000 college and university enrolments in DE in
Canada (Canadian Studies Directorate, 1994)
• DE enrolments at Athabasca University increased from 10,874
(1994/95) to 12,853 (1997/98), 18.2% or 5.7% per year
PIONEERS OF DE IN CANADA
• Course registrations increased from 20,641 (1994/95) to 25,312 (1997/98),
22.6% or 7.0% per year
-(Athabasca University, 1997/98 Calendar)
Other trends in DE in Canada:
• Drop in the average age of distance learners
• Increase in registrations and course loads
• Increase in the number of female students
Other DE Settings:
• In-House training for employees and professional associations, e.g.
Institute of Canadian Bankers, Certified General Accountants of
Canada;
• Alberta Distance Education Training Association (ADETA)
FACTORS UNDERLYING DE GROWTH
• Economic growth
• Rising Incomes
• Increasing public expenditures on PSE
• Population Growth
• Geographic separation of linguistic minority groups
• Continuing education needs of populations living far from urban
centres
• Flexibility inherent in DE, e.g. any time anywhere
• Computer and Internet innovations
IMPORTANCE OF DE
• Empirical research has shown that academic achievements of DE
learners are comparable to that of on-campus taught face-to-face
• Higher enrolments in DE means economic effectiveness of resource use
since DE institutions don’t need additional expenditures like new
classrooms in order to expand
Value-added by DE:
• Increasing student access
• Serving rural communities
• Expanding student educational choices
• Ability of DE to transcend geographical boundaries
IMPORTANCE OF DE
• These developments make it all the more imperative to devote time and
resources toward research to learn more about the increasing
enrolment demand, institutional and general factors fuelling this
growth, and the individual characteristics of the students who are
being served.
DETERMINANTS OF DE DEMAND
Past research indicates that demand for post-secondary education is influenced by
a complex set of factors, including:
•
Expected stream of future benefits (Shultz, 1961; Becker, 1964; Bishop, 1977;
Campbell and Siegel, 1971; Fiorito and Dauffenbach, 1982; Freeman, 1986;
Leslie and Brinkman, 1988; Willis and Rosen, 1979);
•
Family income as part of student’s investment capital (Bishop, 1977; Gorman,
1983; Galper and Dunn, 1969; Schwartz, 1986; Spies, 1978)
•
Price (Tuition & Fees) (Funk, 1972; Heller, 1997; Campbell and Siegel (1967;
Radner and Miller, 1975; Funk, 1984; Ehrenberg, Sherman; and Schwartz,
1986; Leslie and Brinkman, 1987; Jackson and Weathersby, 1975).
•
Employment expectations and
•
Family background characteristics (Albert, 2008).
•
.
DTERMINANTS OF DE DEMAND
However the following determinants have not been explored:
• Number of Programs
• Number of Distance & Online Courses,
• Marketing Expenditures on advertising and recruitment activities,
• The Canadian University Participation Rates (UPR)
ANALYTICAL FRAMEWORK
THE MODEL:
• A formal statement of the general model is given as:
• Qdt = f1 (Pt, UPRt, GDPt, MktExpt, #DistCrst, Unempt)
• Where:
• Qdt is the demand for distance and online education in year t
• Pt is the real tuition & fees in year t (money tuition deflated by the
Consumer Price Index, CPI)
• UPRt is the proportion of the 18-24 year old in post-secondary education
in year t in Canada
• GDPt per capita, here represents average household income as well as an
indicator of how well the Canadian economy is doing in year t.
• MktExpt is the average expenditure on marketing and recruitment
activities in year t
• DistCrst is the number of distance and online courses available in year t.
• Unempt is the unemployment rate in year t in Canada.
THE REGRESSION FUNCTION
To estimate the general model, the following multiple regression version was used:
•
Qt = b0 + b1x1t + b2x2t + b3x3t + b4x4t + b5x5t + b6x6t + et
Where:
• Qt = response or dependent variable, i. e. enrolments/registrations in fiscal
year t (= 1975/76, 1976/77 …….2007/08)
•
b0 = intercept of the regression model, which is the mean value of the response
variable when all the predictor variables are zero
•
x1t = represents tuition & fees or the price per a 3-credit course paid by
students in a fiscal year t deflated by the Consumer Price Index
•
x2t = represents the Canada University Participation Rate in fiscal year t
•
x3t = represents the effect of the Gross Domestic Product, GDP, that is the state
of the economy, on enrolments/registrations in fiscal year t. The GDP may also
stand for the role of income in demand for education
THE REGRESSION FUNCTION
• x4t = represents marketing expenditures on advertising and other
recruitment activities in fiscal year t
• x5t = represents number of distance education courses available in fiscal
year t
• x6t = represents the Canadian unemployment rate in fiscal year t
• et = the stochastic error term in fiscal year t, that is, the effect of potential
variables not included here in the model under consideration
• b1, b2, b3, b4, b5, b6 are the coefficients or parameters of the explanatory or
predictor variables to be estimated
MODEL ASSUMPTIONS
• The model is based on the following classical linear regression
assumptions:
• E (et) = 0 for all t, that is the expected value of the errors is zero for all
possible sets of given values of x1, x2, x3, x4, x5 and x6., that is: E |ei| = 0
for i = 1, 2, 3, 4, 5, 6.
• The error term e is independent of each of the m independent
variables x1, x2, x3, x4, x5 & x6 i.e. E (xktet) = 0 for all k = 1, 2, 3, 4, ..m
• The errors, e, for all possible sets of given values of x1, x2, x3, x4, x5 &
x6 are normally distributed.
• Any two errors ek and ej are independent. Their covariance is zero: E
(ekej) = 0 for k ≠ j
• The variance of the errors is finite, and is the same for all given values
of x1, x2 ...xm. That is V |ei| = s2 is a constant for I = 1, 2, n
HYPOTHESES
The regression model was estimated using STATA statistical software to test
the following hypotheses :
• That the price (tuition) effect upon demand is negative (b1<0)
• That the UPR effect upon demand is negative (b2<0)
• That the income effect upon demand is positive (b3>0)
• That the marketing effect upon demand is positive (b4>0)
• That the distance courses effect on demand is positive (b5>0)
• That the unemployment effect on demand is negative (b6<0)
ESTIMATED MODELS AND MODEL DIAGNOSTICS
The Macro Perspectives
• The following model was estimated at the macro level:
• Qt = b0 + b1x1t + b2x2t + b3x3t + et
The Micro Perspectives
• The following model was estimated at the micro level:
• Qt = b0 + b1x1t + b2x2t + b3x3t + b5x5t + et
MODEL DIAGNOSTICS
The estimated models were diagnosed and tested for the presence of:
• Autocorrelation (serial correlation: potential values of the residuals follow
a particular pattern): Residual plots & D-W test
• Heteroscedasticity (V (et) = s2 for all j): residual plots against the predicted
values of the dependent variable & Brausch-Pagan Test
• Multicollinearity (if independent variables are dependent upon each other
or are collinear): Tests: rx1x2 & VIF
Results:
• No compelling evidence of a serious presence of any of these data problems
were found
• No strong evidence of model misspecifications were found
ESTIMATED MACRO MODELS
Table 1 Estimated Results of the Macro Enrolment Demand Model (All Students)
Variables
Estimated
Coefficients
Standard
Error
Standardiz
ed Beta (b)
t-value
Sign p>|t|
Constant (b0)
317,892.5***
29,703.45
0.000
10.702
0.0000
Tuition & Fees (b1)
-3,927.07**
1,262.56
-1.1191
-3.110
0.0077
13.150
Income (GDP) (b2)
#Courses(b3)
85.0738**
21.22
0.6903
4.009
0.0013
3.006
109.7501**
32.15
Mean Variance Inflation Factor (V. I. F.)
1.0302
3.414
0.0042
9.245
R2 = 0.862
Adjusted R2 = 0.833
Correlation R = 0.929
F-value= 29.19 p-value= 0.0000
D.W.=1.48
*p<0.05; ** p<0.01; ***p<0.001
VIF
8.467
ESTIMATED RESULTS
Table 2 Estimated Results of the Macro Enrolment Demand Model (Male Students)
Estimated
Coefficients
Standard
Error
Standardize
d Beta (b)
t-value
Sign p>|t|
VIF
121,551.44***
12,186.49
0.000
9.974
0.0000
13.150
-1,466.68**
517.99
-1.2022
-2.831
0.0133
3.006
30.59***
8.71
0.7133
3.513
0.0034
9.245
#Courses(b3)
46.58**
13.19
Mean Variance Inflation Factor (V. I. F.)
1.2571
3.531
0.0033
13.150
Variables
Constant (b0)
Tuition & Fees (b1)
Income (GDP) (b2)
R2 =
0.808
Adjusted R2 = 0.767
Correlation R = 0.899
F-value= 19.66 p-value= 0.0000
D.W.=1.60
*p<0.05; ** p<0.01; ***p<0.001
8.467
ESTIMATED RESULTS
Table 3 Estimated Results of the Macro Enrolment Demand Model (Female Students)
Estimated
Coefficients
Standard
Error
Standardize
d Beta (b)
t-value
Sign p>|t|
VIF
Tuition & Fees (b1)
196,900.00
-2,491.04
18,060.28
767.66**
0.000
-1.0721
10.902
-3.245
3.17E-08
0.0059
13.150
3.006
Income (GDP) (b2)
54.35
12.90***
0.6663
4.213
0.0009
9.245
#Courses(b3)
64.00
19.55**
0.9072
3.274
0.0055
13.150
Variables
Constant (b0)
Mean Variance Inflation Factor (V. I. F.)
R2 = 0.884
Adjusted R2 = 0.859
Correlation R = 0.940
F-value= 35.47 p-value= 0.0000
D.W.=1.39
*p<0.05; ** p<0.01; ***p<0.001
8.467
PERFORMANCE OF THE MACRO MODELS
• The macro model provides a reasonably very strong fit to the data: R2
=0.86; 0.81 & 0.88 for All, Male & Female students
• Adjusted-R2 =0.83; 0.77 & 0.86 for All, Male & Female students
• The large F-values (significant far beyond 0.001, that is p<0.001) implies
that it is a very strong model
• The estimated multiple correlation coefficients R (0.93; 0.89 & 0.94)
indicate very strong correlation
• Results are consistent with a priori expectations
• The estimated coefficients, b’s, possess the necessary signs and are
statistically significant.
• This indicates that the influence of tuition and fees (price), income and
number of courses on enrolment demand are all significant.
ESTIMATED MICRO MODELS
Table 4 Estimated Results of the Micro Demand Model (All Students)
Variables
Constant (b0)
Estimated
Coefficients
Standard
Error
Standardize
d Beta (b)
t-value
Sign p>|t|
808.26
1,308.1489
0.000
0.618
.5418
953.6708
-0.5453
-4.368
.0002
24.178
Tuition & Fees (b1) -4,165.31***
VIF
Income (GDP) (b2)
18.77***
3.6084
0.7041
5.201
1.78E-05
28.457
#Courses(b3)
29.21**
9.9555
0.5762
2.934
.0068
59.845
MktgExp (b4)
0.0127***
0.0025
0.2634
4.995
3.09E-05
4.321
Mean Variance Inflation Factor (V. I. F.)
R2 = 0.983
Adjusted R2 = 0.980
Correlation R = 0.991
F-value= 381.40 p-value= 0.0000
D.W.=0.76
*p<0.05; ** p<0.01; ***p<0.001
29.2
ESTIMATED MICRO MODELS
Table 5 Estimated Results of the Micro Demand Model (Male Students)
Estimated
Coefficients
Standard
Error
Standardize
d Beta (b)
t-value
Sign p>|t|
671.21
445.39
0.000
1.507
.1434
Tuition & Fees (b1)
-1,167.66**
324.70
-0.5472
-3.596
.0013
24.18
Income (GDP) (b2)
5.65***
1.23
0.7581
4.596
.0001
28.46
#Courses(b3)
7.33*
3.39
0.5173
2.162
.0396
59.85
MktgExp (b4)
0.0036***
0.00
0.2654
4.127
.0003
4.32
Variables
Constant (b0)
Mean Variance Inflation Factor (V. I. F.)
R2 = 0.974
Adjusted R2 = 0.970
Correlation R = 0.987
F-value= 254.87 p-value= 0.0000
D.W.=0.67
*p<0.05; ** p<0.01; ***p<0.001
VIF
29.2
ESTIMATED MICRO MODELS
Table 6 Estimated Results of the Micro Demand Model (Female Students)
Estimated
Coefficients
Standard
Error
Standardize
d Beta (b)
t-value
Sign p>|t|
-481.88
-481.88
0.000
-0.722
0.4766
Tuition & Fees (b1)
-1,984.30***
-1,984.30
-0.4342
-4.077
0.0000
24.18
Income (GDP) (b2)
14.99***
14.99
0.9401
8.142
0.0000
28.46
#Courses(b3)
4.89
4.89
0.1614
0.963
0.3443
59.85
MktgExp (b4)
0.0098***
0.01
0.3383
7.519
0.0000
4.32
Variables
Constant (b0)
Mean Variance Inflation Factor (V. I. F.)
R2 = 0.987
Adjusted R2 = 0.985
Correlation R = 0.994
F-value= 526.64 p-value= 0.0000
D.W.= 1.03
*P<0.05; ** p<0.01; ***p<0.001
VIF
29.2
ANALYSIS OF RESULTS AND POLICY IMPLICATIONS
Introduction
• The estimated results are consistent with all our hypotheses
• The estimated coefficients, b’s, possess the necessary signs and are
statistically significant.
This indicates that the influences of:
• Price (Tuition and Fees)
• Income (GDP)
• Number of Courses
• Marketing Expenditures on advertising and recruitment
on enrolment demand are generally all significant.
ANALYSIS OF RESULTS AND POLICY IMPLICATIONS
PRICE (Tuition & Fees)
• The impact of price on demand for DE is negative and statistically
significant in both models;
• When price rises, demand for DE declines, all other thins remaining
constant
• Price is the second most important predictor of demand by male students
(b=1.202: macro) & second most important predictor of DE (b=0.547:
micro),
• Price is the second most important predictor of demand by female
students (b=1.072: macro) & second most important predictor of DE
(b=0.434: micro)
• This means that price changes are of greater concern to male students
than to female students at Canadian distance institutions in general and
the typical distance institution in particular.
• Increases in price result in the loss of more male enrolments than female
enrolments for DE.
• These results confirm the economic hypothesis that demand for education
is inversely related to price (Jackson & Weathersby, 1975; Bishop, 1977;
Funk, 1972; Corman, 1983).
ANALYSIS OF RESULTS AND POLICY IMPLICATIONS
INCOME (GDP)
• Income has positive effect on demand for DE (Mueller & Rockerbie, 2005)
• It is statistically significant for Canada (macro models) and for the typical
distance education institution in Canada (micro models).
• Income is the third most important predictor of demand by male students
(b=0.713: macro) and first most important predictor of demand by male
students (b=0.758: micro)
• Income is the third most important predictor of demand by female
students (b=0.666: macro) but the first most important predictor of
demand by female students (b=0.940: micro)
• The influence of income on demand for DE is greater for male students
than for female students in Canada in general, but greater for female
students than male students in the typical institution
• This means that increase in income attracts more demand from female
students than from male students in the typical institution.
ANALYSIS OF RESULTS AND POLICY IMPLICATIONS
MARKEING EXPENDITURE
• The marketing expenditure variable has a positive effect on demand for
distance education and is statistically significant
• This means that the more we spend on advertising and other recruitment
activities, the more students will enrol at a typical distance education
university
• Thus a $1.00 increase in marketing expenditures will lead to 0.0127 new
enrolments; $10,000 increase will lead to 127 new enrolments; and a
$100,000 increase will lead to 1,270 new enrolments.
• Marketing and recruitment expenditures are more important to female
students (b=0.338) than to male students (b=0.265) as exemplified by the
estimated standardized beta coefficients
• This means that any dollar amount spent on marketing and recruitment
attracts more female enrolments than male enrolments.
ANALYSIS OF RESULTS AND POLICY IMPLICATIONS
NUMBER OF COURSES
• The estimated course coefficients for both macro and micro models are
consistent with the a priori expectations and are statistically significant
• The availability of distance and online courses appear more important to
male students than female students
• The availability of courses is the first most important predictor of demand
by male students (b=1.257: macro) but the third most important predictor
of demand by male students (0.517) in the micro model
• Availability of courses is the second most important predictor of demand
by female students (b=0.907) in the macro model, but the fourth most
important predictor of demand by female students (b=0.161) in the micro
model
• This means that increase in the availability of distance and online courses
attract greater demand for distance education from male students than
from female students
SUMMARY AND CONCLUSIONS
• The overall results are illuminating and offer some interesting
implications for enrolment demand for distance education in Canada.
• The impact of tuition and fees (price) on demand for distance education is
negative and statistically significant, confirming the economic hypothesis
that demand is inversely related to price.
• Price changes are of greater concern to male students than to female
students at Canadian distance institutions, implying that increases in the
price of distance education would result in more male enrolment losses
than female losses
• The impact of income on the demand for distance education is greater for
male students than for female students in Canada in general.
• However, in a typical distance education institution, the impact of income
on demand for distance education is greater for female students than for
male students
• This suggests that increase in income would attract more demand from
female students than from male students.
SUMMARY AND CONCLUSIONS
• At the typical distance university, marketing and recruitment expenditures
are more important to female students than to male students
• This means that any dollar amount spent on marketing and recruitment
would attract more female enrolments than male enrolments
• Availability of distance and online courses appear to be more important to
male students than to female students.
• Increased availability of courses would attract greater male demand for
distance education than female demand
• The overall importance of the study is its ability to provide a theoretical
and empirical framework for the analysis of demand for distance education
at both national and institutional levels.
THE END
THANK YOU