wp1- cost-benefit analysis of net based higher education

The main purpose of a CBA is to analyse to what extent the resources are used efficiently for the society as a whole.

Cost benefit analysis (CBA) contains identification and valuation of costs and benefits associated with education.

Generally, the benefit of education is the (higher) production value (income) that follows from individuals increasing their human capital and productivity. The costs that are relevant to consider in a CBA are the alternative costs, i.e. the values of the production resources in an alternative use. This means that all resources that are used in the education must be seen as an input in some other activity.

Some general steps that should be included in a CBA.- Identification of benefits.- Identification of costs.- Quantify the costs and benefits.- Calculation of net present values.- Decision criteria.- Sensitivity analysis

WP1-Cost-Benefit Analysis of Net Based Higher Education

WP1- Combining Benefits and Costs: the rates of return to education

Costs and benefits can be combined in several ways in order to do a cost-benefit analysis. The most common methods are:

Rates of return.

Cost-benefit (and benefit-cost) ratios. Net present values.

The rate of return to investment in education is a measure of the future net economic payoff to an individual or society of increasing the amount of education taken (Carnoy, 1995). It is calculated by setting the discounted value of costs (Ci) and benefits (Bi) over time equal to zero and solving for the implicit discount rate r:

We will estimate private rates of return for individuals and rates of return for society, in

which private benefits are added to those accruing to firms and society, and private costs are also summed to costs incurred by firms and society

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WP1- Combining Benefits and Costs: the rates of return to education

Therefore, we will have to estimate two different types of discount rates: The private rate of return to education (rp) through the discounted value of private

costs (PC) and benefits (PB):

The social rate of return to education (rs), defined as the relation between social costs (SC) and benefits (SB):

Where: SB = PB + UB; UB = Benefits accruing firms and society. SC = PC + UC; UC = Costs incurred by firms and society

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WP1- Estimating rates of return. Theoretical models

There are two principal methods used in estimating rates of return to education:

The traditional method: Takes into account calculated annual costs and earnings by education level. To estimate private returns to education direct and indirect costs carried by

individuals are added to opportunity costs (earnings foregone). Private costs are added to public costs to estimate annual social costs for the

social rate of return estimate Annual private and social benefits are calculated from the difference in average

earnings of those who have different levels of education Annual costs and benefits are inserted into equation (*) in order to estimate the

discount rate that makes costs and benefits became equal.

(*)

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The Mincer method:

uses regression analysis to fit a Mincerian human capital earnings function. The classical specification used to estimate the effect of individual schooling on wages has been the

following (Mincer, 1974):

Where: W = is the wage (earnings) S = the years of schooling. E = the experience. X = a set of individual characteristics U = the variation in log-wages not captured by the computed variables.

The parameter measures the percentage increase in wages associated with an additional year of schooling. Under certain conditions (which include the assumption that there are not direct costs of education) can be interpreted as the private rate of return to schooling.

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The reasoning of this procedure is that partial differentiation of lnW with respect to S gives a method of the calculation of rates of return (Carnoy, 1995), in a continuous form:

And also in discrete form:

Where Ws and W0 refer to the earnings of those individuals with s and 0 years of schooling, respectively.

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WP1- Some exemples of rates of return

Returns to investment in education by level, full method, regional averages (%), 2003.


Social returns to investment in education by income level.


Returns to investment in education by level,

2003.

WP1- . Analysis of monetary benefits determinants

The increase of individuals’ level of educational attainment is consistent with an increase of their productivity in the labour market, what explains higher wages for more educated workers.

Wages increases with experience. Moreover, the earnings grow with experience significantly faster for more educates employees than for less educated.


Random effect estimates. Dependent variable: log real hourly earnings


To determine differences in wages between individuals that have attended higher education programmes through online and on-campus methodology we must test how occupational skill requirements and the degree of ICT adoption by industry matches with online and on-campus students skills. To do this we need to assume an implicit relationship between education and ability.

Occupational duties are an unbiased variable to measure the occupational skills required in the labour market. This variable allows the researchers to relate the skills of the people with the occupational tasks. Moreover permits observe the concordance between the work’s tasks and the workers skills.

Spitz’s work “IT Capital, Job Content and Educational Attainment “permit to observe some changes, and new trends in the skills requirement along the period from 1979 to 1999.


Trends in aggregate skills requirements (workers in West Germany and of German Nationality).


Distribution of task intensities by educational groups (workers in West Germany and of German Nationality).


Hypothesis: Holding other things equal, quality online students perform better (are more productive) in high-skilled jobs of

ICT advanced industries and, therefore, have higher earnings.

Mincerian wage functions can be used to analyse this relationship between types of higher education, quality of education, occupation’s traits, ICT uses and wages:

. S = schooling

. E = experience

. X = set of individual variables

. T = type of higher education technology

. A = quality of higher education

. O = occupational title or kind of job

. I = level of technology adoption by industry

. C = degree of competence in the economic sector

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WP2- Student performance of e-learning

The diffusion of ICT infrastructure in higher education tools has induced important changes, not just on the pedagogic sphere, but also related to administrative and organizational issues.

The increasing use of the online learning tools and its diversity allow students have more choices in an online course than they used to have in a traditional face-to-face environment.

Two main questions:

Does the use of ICT affect student performance? Does the use if ICT affect student performance differently depending on the subject?

WP2- Analysis of student performance through production functions

Which variables affect students’ achievement?

The analysis of student performance will allow us testing the relations between achievement, earnings, institutional variables (organisation, methodology, technology) and students’ profile.

Some analysis constrains: the multidimensional nature of educational outputs, the lack of market value measures for some of the educational process results and the joint production of these different educational outputs (Maddala, 1977).

Two alternative approaches to specify the relation between educational inputs and outputs:

The production function The frontier production functions

WP2- Theoretical models

The technical relation that underlies education production functions can be expressed as follows (Hanushek, 1986):

Where:

A = represents the achievement of a student I at period t. Xi = is a vector of ability, attitudes and socio-demographic characteristics for student I at period t. H = is a vector of inputs for university I at period t. Within this group we should include four different set of

variables:

Institutional variables, related to the level of institutional commitment towards ICT adoption. Technological variables, linked to the use of different ICT devices for teaching and learning purposes. Methodological variables. Teachers’ inputs, related to the degree of technology and methodology uses by teachers.

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WP2- Empirical models

The most simple and common functional form to describe the technical relation between inputs and outputs is Cobb-Douglas function, which can be expressed as follows:

or

Cobb-Douglas function has an important constraint, i.e. the fact that substitution elasticity between inputs is equal to one.

Two alternative functional forms: The CES production function:

It can be estimated through the use of different methods, for instance Kmenta method (1962), transformation method by Box and Cox (1964) or procedures to adjust non-lineal models (Zellner, 1971).

The translog production function:

It can be estimated by conventional econometric methods

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WP2- Data needed and survey design

We need to collect information from students who attend different courses or modules where some use ICT while others don’t.

The variables collected trough the survey can be gathered into some general categories: student preparation, student and family characteristics, students’ ability, how students used the course materials, and the characteristics of educational institutions.

If we want to compare the results between the online and the face-to-face methods will be suitable that the survey is responded in the same period.

The socioeconomic characteristics of the country or region must be included by the investigator, in order to obtain the peculiarities and similarities of and between regions.

To evaluate the influence of the diversity of learning tools, the questionnaire also must be focused on how the students used the course materials.

WP2- Data needed and survey design

We must send out a questionnaire to students in order to collect the following information:

Grade (fail, pass, pass with distinction, or something else). This will be our dependent variable, y . Sex Age College grades Type of college exam (science, social science, practical) Numbers of semesters at university level Students attitude (endogenous) Time use (endogenous) Other activities (work, club activities, see Löfgren, 1998)

We will also need the following information to control for other potentially important determinants of student performance: Restricted intake (admission).. Collaboration. To what extend are collaboration part of the teaching process. Class size. Type of exam (written test, exam paper etc.). Teacher (name, sex, education).

WP2- Hypothesis and expected results

There is a consensus that an appropriate use of digital technologies in higher education can have significant positive effects both on students’ attitude and achievement (Talley, 2005).

Empirical results show a worse performance of online students respect to their face-to-face counterparts (Coates et al., 2004; or Brown and Liedholm, 2002). However, these results are not related with students’ characteristics. Brown and Liedholm (2002) conducted an empirical study where can be observed that students

who are enrolled in an online course have better characteristics than the live students.

Are significant these differences?

Brown and Liedholm (2002) conclude that the difference between performances of the two methods is significant.

Coates et al (2004), although its results indicate that students in face-to-face courses use to score better than their online counterparts, argue that this difference was no significant. This difference is due to the importance of the self-selection into online courses and its effects on the determination of students’ outcomes.

Students’ characteristics like ability or prior experience affect in his/her performance

The better results in the exams that live students show can be due, at least in part, to differences in the student effort. Student effort, expressed in hours allocated to study, tend to be higher among live students than online students


The fact that universities supply digital devices does not necessarily mean that these tools are used, since often educators are precisely the ones that remain reluctant to its utilization

in their subjects. One of the possible causes of this reluctance is the fact that the introduction of ICT-

based tools in teaching methods require more time for teachers than with traditional methods ( Becker and Watts,2001).

The benefits of technology may not be uniform across the student characteristics (ability, gender, or prior experience) Brown and Liedholm (student preferences in Using Online Learning Resources) use

the concept of “cognitive styles” to explore the role of differences in student abilities, past learning in the subject, attitudes, and aptitudes make in the explanation of learning achievements.

These authors argue that “ a student’s having a cognitive style is analogous to the student’s having a production function for learning, and indeed, the cognitive style determines the underlying shape of the learning curves or the student’s production function for learning”.


Among the diversity of materials available in the course the students will value better those who consider concordant with their diverse cognitive styles.

To contradict the belief that those instructors that use technologies in their classes spend

more time that those who don’t make use of them. The instructor who use technology with high intensity spend the same amount of time

in their teaching activity that those who are more reticent to use technology tools in their classes. ( Sosin et al, 2004).

No longer concern because the real significant issue is in what manner technology is used at university, teachers and students’ level (Sosin et al., 2004)


Table 3- Fixed- Effect Panel Regression with Institution Cross Group


WP2- Methodological constraints

Econometric models of the production function of education may have some estimation problems related to endogeneity, data censoring, measurement and self-selection (Becker 2001; Becker and Powers, 2001; Sosin et al. 2004).

The data-censoring problem arises if the dependent variable has an upper or lower bound that limits the measurement of the student performance.

OLS (ordinary least- squares) regression specification is the most common econometric model used to measure the differential impact of online courses on educational outcome.

Some inconveniences of this model:

Sosin, K. et al (2004) point out that “econometric models of the production of learning may have estimation problems related to measurement, self-selection data censoring and endogeneity”

Coates et al. (2004) argue that “a potential shortcoming of the OLS regression procedure is that it is possible that an individual’s choice between distance learning and face-to-face instruction is affected by unobservable differences in ability and learning styles. In this case, OLS estimates of the parameters would be biased and inconsistent due to endogeneity”.

If the decision of the mode of instruction selection is related to each student’s expected performance under each method of instruction, OLS is not an appropriate specification.

There are alternative econometric models:

The 2SLS ( two stages least-squares) specification. Switching equations models with endogenous switching

Maximize or minimize the educational production function?

The majority of the authors tend to maximize the educational production function. It means that ICT tools have been created to maximize the gains available to students.

However other authors, like Talley (2005) hold that students will seek the amount of learning that they believe appropriate to earn a desired grade at the minimum cost possible. As Talley conclude “they may be considered cost-minimizers when it comes to learning”.

WP2- Methodological constraints

wp1- cost-benefit analysis of net based higher education

Documents

education costs

identification of costs

alternative costs

indirect costs

valuation of costs

public costs

private benefits

annual social costs