Higher Education Incentives for Economic Development

Education Incentives 1

Higher Education Incentives for Economic Development

by

Ryan MacNeil
MAES Candidate
20184990

for

Dr. Emanuel Carvalho
LED 613: Regional Development
University of Waterloo

April 8, 2004


Higher Education Incentives for Economic Development

A common mantra among Atlantic Canada’s post-secondary student leaders is that

graduates must leave the region if they wish to increase their earnings and repay their

student debt. But what does this mean for regional economic development? Since an

educated population is vital to endogenous growth, government has developed a variety of

interventions to encourage participation in post-secondary education (PSE). The key

intervention has been the student loan. This paper establishes that while government has

been encouraging participation it has unwittingly been encouraging out-migration from

Canada’s poorest provinces. Sky-rocketing tuition has been met by increases in student debt

levels. Many university and college graduates are forced to leave the least-opportune

provinces to earn wages that are sufficient to service their debt loads. Is there an appropriate

policy response that encourages participation in post-secondary education without

encouraging out-migration? Seven funding sources were examined to determine their effect

on migration. The analysis reveals that few existing government interventions meet both of

the criteria identified. The best strategy to encourage participation without encouraging out-

migration appears to be direct reductions in tuition fees, creating a competitive advantage

over fees in other provinces. Tax incentives to encourage parental support also show promise.

Background

Why encourage PSE?

Government policy in Canada is often informed by neoclassical economic theory

(Carvalho, 2005). This theory states quite simply that regional output of a commodity (in the


case of the one sector model) is a function of the capital and labour employed in its

production (see Equation 1). By modifying this formula, it can be shown that an increase in

productivity (defined as output per unit of labour) is the result of an increase in the ratio of

capital to labour (see Equation 2). Therefore, increases in productivity can only be achieved

two ways: labour must remain constant while capital increases, or capital must increase faster

than labour. Unfortunately this simplistic version of the theory suggests that technological

progress has no qualitative effect on productivity. Technology can only manifest as

additional capital inputs and reduced labour inputs.

Acs and Varga (2002) compare neo-classical and endogenous growth theories and

explain that the latter allows for “the modeling of technological change as a result of profit-

motivated investments in knowledge creation by private economic agents” (p. 137). They

argue that the former is limited by assumptions of perfect competition and constant returns

to scale. In fact, technology is not a purely ‘public good’ since knowledge can be “sticky”

(Bourgeois and LeBlanc, 2002) in time and space. Patents and tacit knowledge can create

disparity in technological diffusion. Firms and regions that can develop “sticky” innovations

gain market power and fixed-term monopoly profits (Bourgeois and LeBlanc, 2002).

Endogenous growth theory attributes productivity growth above and beyond a

change in the capital-labour ratio to “innovations”. These can take the form of product or

Equation 1 Equation 2

Q= f (k , L ) Q
= f k
L L


service innovations, process innovations, and organizational innovations (Morgan, 1997;

Bourgeois and LeBlanc, 2002; and Betts, 1998). Product or service innovations can be

incremental changes to existing products or services, or entirely new ones. Process

innovations can either reduce the costs or improve the quality of production (for example,

just-in-time inventory systems). Organizational innovations involve some form of structural

advantage, such as the way Walmart coordinates its distribution chain through computerized

inventory systems. Even when experiencing equal capital and labour growth, firms that

successfully implement innovations will see growth in output over those which do not

innovate (Carvalho, 2005). This revelation has encouraged governments to divert resources

from expensive capital-mobilization strategies to innovation catalyzing ones. However,

innovation defies simple government intervention.

A typical government initiative might involve encouraging research and

development. When discussing the downfalls of typical job-creation strategies for declining

regions, Hall (1984) suggests using an existing or “deliberately implanted” research and

development tradition to create an entrepreneurial tradition. He is cautious, and notes, “such

bold strategies may succeed, but they are likely to take a long time to produce substantial

results…no single strategy, but rather a combination of different approaches, will be

appropriate” (p. 35). Despite this hesitation, and the tradition of peer-juried awarding of

university research grants, Hall concludes with a call for “the establishment of regional

quotas to the Research Councils” (in the UK, USA and Canada). Indeed, there is evidence

that the Canadian government’s university research grants neglect disadvantaged regions.


Over its first five years, the Canada Foundation for Innovation invested only 3.2% of its total

contributions in Atlantic Canada (Beaudin and Breau, 2001, p. 133). But only measuring

innovation in terms of gross expenditures on research and development is inappropriate.

GERD is “meant to reflect the degree of innovative effort and intent, not necessarily

innovative potential and success” (Bourgeois and LeBlanc, 2002, p. 170). Despite a low level

of government R&D funding grants, Bourgeois and LeBlanc found that Atlantic Canada firms

in knowledge intensive industries (computer services, engineering consultant services, and

other scientific and computer services) have innovation rates near the national average

(2002, p. 71). However, this innovation is much less likely to involve the introduction of

new capital intensive technologies than elsewhere in Canada (financial capital is lacking).

They say that, “studies in the last ten years are increasingly rejecting R&D as a master key

that unlocks a linear innovation process, seeing it instead as one of several pieces to the

innovation puzzle” (p. 170).

There is a myth that innovation is unique to high-technology industries and only

happens in R&D laboratories. Bourgeois and LeBlanc, as well as Beaudin and Breau, note the

importance of innovation to firms in the primary sector. For example, in the Atlantic fish

processing sector between 1988 and 1996, the number of labour-hours declined 40% but the

value-added per hour rose 35% (Beaudin and Breau, 2001, p. 89). These industries “acquire

ideas not from in-house R&D but by tapping into the knowledge and ingenuity of their

workers, suppliers and customers – by networking with research institutions, universities,

competitors, governments, and other stakeholders” (Bourgeois and LeBlanc, 2002, p. 18).


Indeed, there is a burgeoning volume of research on the social-embeddedness of innovation.

Noted academics like Saxenian (1994) argue that community networks encourage the free-

flow of ideas and therefore foster continuous innovation. Saxenian is critical of science parks

and other strategies that aim to create replica Silicon Valleys. She concludes that, “ultimately

regions are best served by policies that help companies to learn and respond quickly to

changing conditions – rather than policies that either protect or isolate them from

competition or external change” (p. 166).

All of this is not to diminish the importance of both publicly and privately funded

research and development. Rather, this discussion has demonstrated that there is no simple

solution to the problem of regional disparity. A region’s absorptive capacity is as important as

its ability to develop new technologies. While government might fund and encourage new

technology development in many ways, it cannot neglect the need for a broadly educated

population. Human capital is requisite to both the creation and absorption of innovations.

And social networks are the fabric that enables collaborative innovation. Those regions that

are best able to mobilize the innovativeness of their residents will prosper over the long

term. A basic key to innovation is therefore post-secondary education.

Is there really a problem?

Fenton, Gardner and Singh (2001) recently published an econometric model to

predict the outcome of cuts to PSE in the state of New York. The model demonstrates that

when participation rates fall in response to rising tuition prices, personal income and

personal tax receipts decline. Using a net-present-value accounting model, the authors


conclude that “potential revenue losses quickly dwarf the short-run savings of funding cuts”

(Fenton, Gardner and Singh, 2001, p. 54). However, in the Canadian context there is a

fundamental flaw in this model. As tuition has nearly doubled in Canada over the past

decade, participation rates have also been climbing. More students are paying more money to

attend university and college. This is possible because Canada has a comprehensive system of

government sponsored student loans. This loan system effectively defers the impact of

tuition price hikes so far into the future that the elasticity of demand for PSE is significantly

altered. The PSE consumption decision may not be entirely rational in an economic sense.

Logically, however, the labour market should correct for these higher debt levels. It

appears to be adjusting in two significant ways. First, post-secondary graduates garner a

significant wage premium over their counterparts who did not attend PSE. The Maritime

Provinces Higher Education Commission found that the wage-premium for graduates who

stayed in the Maritimes was 26% (MPHEC, 2002, p. 6). However, the MPHEC also found

that graduates who left the Maritimes saw a 78% wage premium (Ibid.). This appears to be

the second labour-market correction. Graduates migrate in search of higher wages. In the

general population of Canadians, men typically migrate for economic reasons (Finnie, 1998)

while women are more likely to migrate for family reasons (Lin, 1995). However, there is

some indication that post-secondary graduates are different in this regard. Male and female

graduates in the Maritime Provinces are equally likely to cite job-related economic reasons

for migration (MPHEC, 2002). Of all Canadians, young post-secondary graduates are the

most mobile (Looker, 2001, p. 32). Nearly half (45%) of Prince Edward Island residents, one-


in-seven New Brunswickers, and one-in-fifteen Nova Scotians left their home provinces to

go to university (MPHEC, 2002). Those graduates who had attended university outside their

home province were 16% more likely to move at least once after graduation, and nearly one-

in-seven left the region completely. Twenty-eight percent of engineering graduates leave the

region within one year of graduation. They are followed by graduates of math and physical

sciences (20%), information technology (15%), health professions (13.4%) and fine and

applied arts (12.1%).

Indeed, each progressive level of education results in greater proportions of graduates

leaving their home province. MacNeil (2004) found that Canadian graduates at the Ph.D.

level show the greatest propensity for leaving their province of origin. Nearly one-third of

them (29.7%) become leavers, versus 17.0% of masters graduates, 10.9% of undergraduates,

5.8% of college graduates, and 3.4% of trade and vocational graduates. Looker (2001, p. 30)

disputes this link by saying, “…it is not always clear from the data whether those with

higher education are more likely to move, or if those who are more likely to move obtain

higher levels of education”. Since higher levels of education are centralized at larger

universities it is conceivable that master’s and Ph.D. degrees are only attained by those

willing to move.

Three pieces of evidence suggest that Looker’s critique is unfounded. First, the

propensity among graduates to migrate is not geographically consistent with this logic (see

Figure 1). Atlantic graduates are nearly twice as likely to leave their home provinces as


graduates from anywhere else in the country1. Nineteen-point-five percent of Atlantic

Canadians leave their province of origin at some point either before or after their graduation.

This is trend extends not only to those who originate in Atlantic Canada, but also to all those

who studied2 or were interviewed3 in an Atlantic Province. Therefore, the region of Canada

with the greatest provincial out-migration (Atlantic Canada) has the highest per-capita

number of post-secondary institutions. It also has the weakest economy. This suggests that

migration is more closely linked to economic circumstances than access to higher education.

25

20
Leavers (%)

Atlantic
15
Quebec
10 Ontario
West
5

0
Origin Study Interview

Figure 1. Percent of graduates who became leavers by region of origin, study and interview (MacNeil, 2004).

Second, tuition is correlated with out-migration rates among 20-24 year olds (see

Figure 2).

1 The effect of “region of residence pre-1995” on interprovincial migrant class is statistically significant, L2 = 9,374.229, df =
8, p = .000 (MacNeil, 2004).
2 The effect of “region of institution” on interprovincial migrant class is statistically significant, L2 = 10,525.658, df = 6, p =

.000 (MacNeil, 2004).
3 The effect of “region of interview” on interprovincial migrant class is statistically significant, L2 = 10,126.103, df = 6, p =

.000 (MacNeil, 2004).


Tuition vs. Out-Migration in the Atlantic Provinces

12.00%
Out-Migration of 20-24 year olds (%)

10.00% NL
PE
8.00% NS
NB
6.00%
Linear (NL)
4.00% Linear (PE)
Linear (NS)
2.00% Linear (NB)

0.00%
$- $1,000 $2,000 $3,000 $4,000 $5,000 $6,000
Average Tuition ($)

Figure 2. Linear regression of average tuition prices vs. out-migration rates among 20-24 year olds, Atlantic
Provinces (Prepared by R. MacNeil with data from CANSIM Tables 051-0001 and 051-0012).
Note: Pearson’s Product Moment Correlation Coefficients (r): NL = +0.88, PE = +0.63, NS = +0.38, NB = +0.85.

Tuition vs. Out-Migration in the Central Provinces

1.80%
Out-Migration of 20-24 year olds (%)

1.60%
1.40%
1.20% ON
1.00% PQ
0.80% Linear (ON)
0.60% Linear (PQ)

0.40%
0.20%
0.00%
$- $1,000 $2,000 $3,000 $4,000 $5,000 $6,000
Average Tuition ($)

Figure 3. Linear regression of average tuition prices vs. out-migration rates among 20-24 year olds, Ontario and
Quebec (Prepared by R. MacNeil with data from CANSIM Tables 051-0001 and 051-0012).
Note: Pearson’s Product Moment Correlation Coefficients (r): ON = -0.71, PQ = -0.72.


In the Atlantic Provinces there is a strong positive association between higher out-

migration rates and higher tuition levels since 1992 (with the exception of Nova Scotia

which has a weaker association). This is not simply a time effect. Tuition in Newfoundland

and Labrador has declined over the past four years and so have migration rates. In Atlantic

Canada higher tuition levels are in some way associated with higher out-migration rates.

Meanwhile, in the central provinces of Ontario and Quebec there is a strong negative

association between tuition and out-migration (see Figure 3). Here, higher tuition levels are

in some way associated with lower out-migration rates. The pattern is simple: when tuition

increases, more young people leave the poor provinces and more stay in the rich provinces.

Since debt is likely the real causal factor, the relationship identified above is not perfect.

Unfortunately average student debt data is not readily available.

The third and final reason for rejecting Looker’s chicken-and-egg argument is that

graduates with greater debt are more likely to become leavers (see Figure 4). The incidence

of leavers is lowest among graduates who borrowed a government student loan but had paid

it off by graduation (0.8%). It is highest (5.9%) among those who borrowed the most (over

$30,000).

It is now clear that an educated population is a vital component to economic growth,

but migration rates are related to student debt. Government must therefore attempt to

encourage post-secondary participation without losing new graduates to out-migration.

These two criteria should factor into any higher education incentive policy.


Figure 4. Percentage of leavers among graduates in each loan value grouping (MacNeil, 2004).

Note. The effect of government student loan value (at graduation) on migrant class is statistically significant, L2 = 719.391, df
= 16, p = .000.

Method

Data Source

This study used a public-use micro data file provided by the Government of Canada

under the Data Liberation Initiative. The file contains all records resulting from Statistics

Canada’s Survey of 1995 Graduates in 1997. It is the latest public release in a series of cohort

surveys conducted since 1978. The primary objective of the survey was to collect data on

labour market trends among post-secondary graduates. The sample size is sufficiently large to

support generalization of the findings over the entire population of the class of 1995. This

current study should, however, be considered only an exploratory analysis because the data

is from a cohort that graduated ten years ago.


Population and Sample

The survey's population is all graduates from Canadian public post-secondary

education institutions who completed the requirements for degrees, diplomas, or certificates

during the 1995 calendar year (298,154 individuals). It does not include graduates of private

career/trade colleges and training institutes. With the exception of three small institutions,

all 402 public post-secondary institutions supplied graduation lists. A sample was drawn from

these lists first by stratifying the population into five levels:

- skilled trades
- college
- undergraduate (degrees, diplomas, and certificates)
- masters level (degrees, diplomas, and certificates)
- doctorate

The population was further stratified into nine fields of study for university and

career/technical programs and eight fields of study for the trade/vocational programs. An

independent systematic random sample was subsequently selected from each stratum. The

resulting sample size is 61,759 graduates. Of that sample, 43,040 graduates responded to the

survey (Statistics Canada, 1999). Statistics Canada includes weighting values for all records in

the data file (see the survey documentation, pp. 28 and 61 – 66). These weights have been

used to calculate estimates throughout the course of the study.

Collection and Format

The Survey of 1995 Graduates in 1997 was conducted from May to July, 1997, using a

computer-assisted telephone interview (CATI) methodology (Statistics Canada, 1999). A

conversion script for the SPSS 9.0® file format is provided by the Data Liberation Initiative.


Procedure

The Survey of 1995 Graduates in 1997 data file was analyzed in SPSS 12.0®. The

analysis included the use of basic descriptive statistics, but the primary tool of inquiry was

Pearson’s Chi-Square (denoted as L2 throughout the text). This non-parametric method was

necessary because most of the variables are nominal in nature. Variables in the dataset that

would otherwise be useful in a parametric analysis (such as loan value) are grouped such that

they could not be reorganized into ordinal sets. Violations of the assumption of homogeneity

of variance precluded the use of other non-parametric tests on the data set. It was possible to

calculate the significance of variations in the sample with the chi-squared statistic for two

and three way cross tabulations. A confidence level of 95% was used to interpret all

significance tests.

The three youth migrant classes presented in Rural Youth : Stayers, Leavers and

Return Migrants (Dupuy et al, 2000) were used. For the purposes of this study, migration was

defined as the movement between provinces over the two year study period. To this end, the

values found in the “interprovincial migration for education” variable were recoded. Table 1

is a comparison of the original and new migration variables. Hypothesis testing compared the

post-secondary graduates who fall into the three migrant classes.


Table 1. Recoding of Migration Data into the Three Migrant Classes
Interprovincial Migrant Class (new Interprovincial Migration for Education (old variable)
variable)
Stayers (Value = 0) Non-migrant (Value = 1)
Leavers (Value = 1) Migrant before graduation, not returning to province of origin after
graduation (Value = 3)
Migrant after graduation, not before (Value = 4)
Migrant before and after graduation, not returning to province of
origin (Value = 5)
Return Migrants (Value = 2) Migrant before graduation, returning to the province of origin after
graduation (Value = 2)

Note. The SPSS conversion file provided by the Data Liberation Initiative erroneously switched the definitions for values 2
and 3 in the old variable. This error was corrected using the data codebook.

Limitations

The results of this study were limited by the data available. Unfortunately Statistics

Canada has yet to release the responses collected from a five-year post-graduation follow-up

with this sample in the year 2000. Access to the extra three years of labour market and

migration information would add significantly to the value of this study’s findings. Also, data

from a more recent cohort of graduates would provide more reliable and actionable results.

This study’s definition of migration is also limited by the data available. Detailed

migration information has been suppressed in the public release of this data. Typically

migration is defined in terms of relocation over a specific measured distance, between

communities, or between rural and urban settings. This data set identifies graduates based

only on their region of origin, study, and interview. Despite the focus on regional data there

is one variable that identifies respondents by their inter-provincial migration patterns.

Therefore, this study must define migration as relocation between provinces.


Results

Government Sponsored Student Loans

Surprisingly, graduates who used government student loan programs were less likely

to become leavers than those who did not (see Table 2). While the difference is only 0.7%, it

is statistically significant. This only confirms that government student loan programs, in and

of themselves, do not encourage migration. In fact, the effect of loan programs on migration

is so negligible that, when used as one of two main funding sources, it is insignificant4.

Table 2. Incidence of the Three Migrant Classes by Use of Government Loans.

Interprovincial Migrant Class

Return
Stayers Leavers Migrants Total
Use government Yes Count 113,423 10,082 473 123,978
sponsored
Percentage 91.5% 8.1% .4% 100.0%
student loan
program? No Count 153,151 14,775 698 168,624
Percentage 90.8% 8.8% .4% 100.0%
Total Count 266,574 24,857 1,171 292,602
Percentage 91.1% 8.5% .4% 100.0%

Note. The effect of using a government student loan program on interprovincial migrant class is statistically significant, L2 =
38.700, df = 2, p = .000.

Other Funding Sources

Government student loans are not the only funding source graduates used to finance

their education. Respondents to the Statistics Canada survey were asked to identify their two

main funding sources. Fully half of the major funding sources cited had no statistically

4The effect of government student loans as one of two main funding sources is not statistically significant, L2 = 7.292, df = 2,
p = .026.


significant effect on interprovincial migrant class. These sources were: spouse/partner5,

student loans4, other loans6, and employment earnings7. Conversely, migrant class was

affected by those funding sources that did not place a direct financial burden on the

graduate. Those who used scholarships, awards and fellowships as one of two main funding

sources were the most likely graduates to be leavers (see Table 3). Graduates who did not use

parents and worker’s compensation as their funding sources were more likely to be leavers.

Table 3. The Incidence (%) of Leavers by Main Funding Sources.

Used as one of two main
funding sources?
Funding Source Yes No
Parentsa 8.0% 8.7%
Scholarships / awards / fellowshipsb 14.5% 8.1%
Grants / bursariesc 9.0% 8.5%
Worker’s compensationd 2.5% 8.6%
Note. Those funding sources that had an insignificant effect on migrant class have been excluded.
aThe effect of financial support from parents (as one of two main funding sources) on interprovincial migrant class is
statistically significant, L2 = 49.865, df = 2, p = .000.
bThe effect of financial support from scholarships / awards / fellowships (as one of two main funding sources) on

interprovincial migrant class is statistically significant, L2 = 866.532, df = 2, p = .000.
cThe effect of financial support from grants / bursaries (as one of two main funding sources) on interprovincial migrant class

is statistically significant, L2 = 58.875, df = 2, p = .000.
dThe effect of financial support from worker’s compensation premiums (as one of two main funding sources) on

interprovincial migrant class is statistically significant, L2 = 140.156, df = 2, p = .000.

Repayment Assistance versus Needs-based Grants

An important sideline to the relationship between student debt and migration is the

remedial effect of debt relief programs. The results indicate that loan repayment assistance

5 The effect of financial support from a spouse/partner (as one of two main funding sources) on interprovincial migrant class
is not statistically significant, L2 = 6.819, df = 2, p = .033.
6 The effect of borrowing “other” non-government loans (as one of two main funding sources) on interprovincial migrant

class is not statistically significant, L2 = 2.009, df = 2, p = .366.
7 The effect of employment earnings (as one of two main funding sources) on interprovincial migrant class is not statistically

significant, L2 = 6.590, df = 2, p = .037.


has an insignificant effect on interprovincial migrant class8. This is a discouraging statistic for

those provinces that currently have debt relief programs. Although, debt relief programs

with specific residency requirements (ie. bonus relief for stayers) are only a recent

government strategy. The graduates of 1995 did not benefit from repayment assistance

programs with stayer-targeted rewards. Regardless, the fact that repayment assistance had an

insignificant effect on migration in the mid-nineties begs for an investigation of such

strategies today. More recent evidence is required.

Many post-secondary student leaders would respond to the previous finding by

trumpeting a need-based grant strategy. However, the evidence for such a program is even

more discouraging. Graduates who received need-based grants or bursaries were 1.1% more

likely to have become leavers than those who did not (see Table 4). The grants had no effect

on return migration. This is surprising given that such grants serve to reduce the debt loads

that have been found to encourage migration. Since those who received such grants were the

neediest, it may be that the grants are simply not reducing the debt to a point where these

graduates can become stayers. Perhaps an exploration of the mechanics of grant and bursary

programs is required. Unfortunately the necessary data is not available in this dataset.

8 The effect of “assistance from government/lenders to repay student loans” on interprovincial migrant class is not
statistically significant, L2 = 1.972, df = 2, p = .373.


Table 4. Incidence of Stayers, Leavers and Return Migrants by Receipt of Need-Based Grants / Bursaries.

Interprovincial Migrant Class
Return
Stayers Leavers Migrants Total
Receive Yes Count 40,394 4,205 165 44,764
need-based Percentage 90.2% 9.4% .4% 100.0%
grants / No Count 225,760 20,559 1,005 247,324
bursaries
Percentage 91.3% 8.3% .4% 100.0%
Total Count 266,154 24,764 1,170 292,088
Percentage 91.1% 8.5% .4% 100.0%
Note. The effect of “need-based grants/bursaries” on self-employment is statistically significant, L2 = 58.132, df = 2, p = .000.

Conclusions

One of the many challenges facing governments in Canada is this issue of post-

secondary education. Government should encourage PSE participation as an economic

development strategy. But it is evident that some incentives encourage graduates to leave

disadvantaged provinces. Few existing interventions meet both of the criteria identified.

Scholarships, award and fellowships facilitate migration, as do grants and bursaries. The best

strategy appears to be direct reductions in tuition fees. A province that undertakes this

strategy would create a competitive pricing advantage over other provinces while reducing

the debt-servicing burden. Tax incentives to encourage parental support also show promise.

Surprisingly, debt repayment assistance does not appear to have a significant effect on

migration. This is discouraging news for those provinces whose debt-relief programs are

specifically targeted at reducing out-migration, but more research is required.

The evidence presented here does little to support existing government strategies.


Most provincial governments continue to cut PSE funding, increasing tuition9. To counteract

these increases, provinces like Saskatchewan are providing scholarship and bursary

programs. Scholarships and bursaries may make PSE more accessible, but they exacerbate the

problem of out-migration. Perhaps the worst strategy in this regard was a measure in the

2004 federal budget to increase the maximum allowable student loan. More student debt will

undoubtedly push graduates out of the poorest provinces.

Three provinces have strategies that hold up well under these findings. Manitoba

reduced tuition by 10% in 2000 and has since maintained a tuition fee freeze. Quebec has

deliberately maintained the lowest university tuition in the country (and free college tuition)

for Quebecers. University tuition fees in Quebec have been frozen for 15 of the past 20 years.

Finally, Newfoundland and Labrador now has the second lowest tuition fees in the country

following 10% reductions in 2000 and 2001. Newfoundland and Labrador was the first

province to introduce a loan repayment assistance program tied to a stay-at-home incentive.

The territories have used a similar repayment incentive for some time to address the fact that

all territorial residents must leave to obtain a post-secondary education.

Clearly more research is needed on grant size and more recent data is needed on stay-

at-home debt relief programs. Also, it must be noted that even though some of the

interventions identified had negative or negligible effect on migration their effect on

improving accessibility for low-income earners could be very significant.

9 Details of government programs obtained from CFS, 2005 and provincial government websites.


Works Cited

Acs, Z. and A. Varga (2002). “Geography, Endogenous Growth and Innovation,” International
Regional Science Review 25 (1): 132-148.

Beaudin, M. and S. Breau (2001). Employment, Skills, and the Knowledge Economy in Atlantic
Canada. Maritime Series, Monographs. Moncton: The Canadian Institute for Research on Regional
Development.

Betts, Y. (1998). “II. Resources and Technology: The Implications of Technological Change for
Human Resource Policy,” Canada in the 21st Century. Ottawa: Industry Canada Research
Publications Program.

Bourgeois, Y. and S. LeBlanc (2002). Innovation in Atlantic Canada. Maritime Series, Monographs.
Moncton: The Canadian Institute for Research on Regional Development.

Canadian Federation of Students (2005). “Tuition Fees in Canada: A Pan-Canadian Perspective on
Educational User Fees,” Fact Sheets 11 (1): 1 – 2. Accessible online at http://www.cfs-
fcee.ca/html/english/research/factsheets/tuitionfees2005.pdf

Carvalho, E. (2005). LED 613: Regional Development, Lecture. February 2, 2005. Waterloo:
University of Waterloo.

Dupuy, R., F. Mayer, and R. Morissette for the Atlantic Canada Opportunities Agency (2000). Rural
Youth : Stayers, Leavers and Return Migrants. Ottawa: Her Majesty the Queen in Right of Canada.

Fenton, R.J., J. Gardner and S. Singh (2001). “Rethinking Cuts in Public Higher Education: An
American Example,” Education Economics 9 (1): 53 – 68.

Finnie, R. (1998). Interprovincial Mobility in Canada: A Longitudinal Analysis. Ottawa: Human
Resources Development Canada.

-----. (2000). Who Moves? A Panel Logit Model Analysis of Inter-Provincial Migration in Canada.
Ottawa: Business and Labour Market Analysis Division, Statistics Canada.

-----. (2002). “Student Loans: Borrowing and Burden.” Education Quarterly Review 8 (4): 28 – 42.

Hall, P. (1984). “The New Geography of Innovation,” in C. Bryant (ed.) Waterloo Lectures in
Geography, Volume 1, Economic Development (pp. 29 – 38). Waterloo: Department of Geography,
University of Waterloo.

Lin, Z. (1995). Interprovincial Labour Mobility in Canada: The Role of Unemployment Insurance and
Social Assistance. Ottawa: Human Resources Development Canada.

Looker, E.D. (2001). Policy Research Issues for Canadian Youth: An Overview of Human Capital in
Rural and Urban Areas. Hull: Applied Research Branch, Human Resources Development Canada.


MacNeil, R.T. (2004). Disadvantaged Region Seeks Enterprising Graduates: A Target Market for
Entrepreneurship Recruitment and Repatriation in Atlantic Canada. Honours Thesis, Faculty of
Business Administration and Tourism, Mount Saint Vincent University, Halifax.

Maritime Provinces Higher Education Commission (2002). “Who Stays and Who Leaves: Mobility
Pattersn of Maritime University Graduates, Class of 1996 in 1997 and 2000,” Trends in Maritime
Higher Education 1

Morgan, K. (1997). “The Learning Region: Institutions, Innovation and Regional Renewal,” Regional
Studies 31 (5): 491 – 503.

Statistics Canada (1999). Survey of 1995 Graduates in 1997 [machine readable data file, user manual
and codebook]. Ottawa: Her Majesty the Queen in Right of Canada.

-----. (2005). Estimates of population, by age group and sex, Canada, provinces and territories, annual
(1990-2003). Ottawa: Statistics Canada. (CANSIM Table 051-0001).

-----. (2005). Interprovincial migrants, by age group and sex, Canada, provinces and territories,
annual (1990-2003). Ottawa: Statistics Canada. (CANSIM Table 051-0012).

Higher Education Incentives for Economic Development

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (10)

Similar to Higher Education Incentives for Economic Development

Similar to Higher Education Incentives for Economic Development (20)

More from Ryan MacNeil

More from Ryan MacNeil (18)

Recently uploaded

Recently uploaded (20)

Higher Education Incentives for Economic Development