https://doi.org/10.7906Interdisciplinary Description of Complex Systems1334-46841334-4676Hrvatsko interdisciplinarno društvoCroatian Interdisciplinary SocietyIvana Lučića 1, 10000 Zagreb
petra.cacic@indecs.euhttp://www.idd.com.hr10.7906/indecs.19.2.2Original scientific paperMeasuring Returns on Investment in Education: Lessons for Sustainable and Innovative Education PolicyJahicHatidza*1Pilav-VelicAmilaSchool of Economics and Business, University of Sarajevo
Sarajevo, Bosnia and HerzegovinaTrg oslobodjenja – Alija Izetbegovic 1, 71 000 Sarajevo, Bosnia and Herzegovina
hatidza.jahic@efsa.unsa.ba30620211921892092772020532021CC BY 4.0Puni tekst objavljenih radova besplatno se smije koristiti za osobne, edukacijske ili istraživačke svrhe uz poštovanje autorskih prava autora i izdavača. Korisnici radove smiju besplatno čitati, preuzimati, kopirati, distribuirati, tiskati, prerađivati ili koristiti ih na druge zakonite načine, uz ispravno navođenje izvornika i nekomercijalnu svrhu uporabe.The usage of full-text of the articles can be used exclusively for personal, research-related or educational purposes, with regard to the authors' and publishers' rights. The users are allowed to read, download, copy, distribute, print and transform or use them for any other lawful purpose as long as they attribute the source in an appropriate manner and for the non-commercial purpose of the usage.
The relationship between education and labor market is significant and complex. Education increases employment opportunities and reduces the chance of occurrence and duration of unemployment. Earnings, among other things, represent private returns on investment in education and are in the center of the analysis of this article. The main aim of this article is to estimate private and social returns on investment in primary, secondary and tertiary education in selected old and new member states of the European Union (EU) by using two methods (Earnings function and Short-cut method) based on the Mincer equation. Results have shown that there is no statistically significant difference between the estimated private and social returns on investment in primary, secondary and tertiary education in groups of old and new EU members. New members converge towards the old members, at least when it comes to returns on investment in these three education levels. The results also indicate the existence of low and negative returns on investment in education in both old and new EU members. Thus, this article with its new findings contributes significantly to the literature that studies the universality of conclusions on returns on investment in education and the methodology that is used.
educationeducation policyinvestmentreturnMincer equationEuropean Union
The analysis of the relationship between human capital and economic growth has been
in the center of attention of scientists since the second half of the last century.
However, it is only with New Economic Growth Theories, especially Human Capital
Theory, that human capital becomes involved and becomes one of the key factors in
explaining the process of economic growth as seen in [19] and others. Undoubtedly, a
significant contribution is reflected in the promotion of the idea that investing in
human capital has multiple positive effects on both the individual and society as a
whole. These effects have been termed human capital externalities in research [1012]
also point out that investing in human capital leads to technological progress and
innovation, and ultimately increases the productivity of other factors in the growth
process. In addition, Barro[13] sees the
greatest contribution of the new models in the fact that economic growth can grow
indefinitely because the returns on investment in human capital will not fall with
the growth and development of the economy as assumed in earlier theories. Becker
[14] who emphasizes the "economic
importance of human capital, especially education," in economic growth and who
believes that "only a small portion of growth and income can be explained by
available physical capital", and Schultz[15],
for whom education is a key factor which leads to the realization of different
levels of wages in the labor market (growth of individual investments in education,
regardless of gender and race will lead to increase in wages) are among key authors
of Human Capital Theory. The state appears to be the most important investor in
education through appropriate institutions that are part of the public spending
system. However, individuals also invest in education. Levin, Schultz, Mincer [1,[15-[18], and Becker [19-[20]
argued that all consumption aimed at improving productivity is an investment in
human capital. Becker [20]states that of all
these investments, the most important is the investment in education and that "rates
of returns are the best and most comprehensive way to measure the economic effects
of education."
Main aim of this article is to empirically estimate the rates of returns on investment in education for three levels of education (primary, secondary and tertiary) in old and new members of European Union (EU). The old EU members are considered to be the EU15, i.e. 15 countries that gained full membership before 2004, while the new members are considered to be the countries from the last three enlargements, namely those from 2004, 2007 and 2013, i.e. 13 new members of the European Union. The defined time period for which the return on investment in education is estimated is from 1985 to 2014. For the purpose of empirical analyses, the data for the mem
ber states of the European Union in the defined period of time are available in International Social Survey Program (ISSP) database. Estimates of the return on investment in education have been made by using two methods based on the Mincer equation [18] which has its theoretical background in the Human Capital Theory. Those methods are Earnings function and Short-cut method. This article seeks to examine the universality of the conclusions on returns on investment in primary, secondary and tertiary education and the effects of the chosen methodology on the estimation. In addition, unlike most research on this topic, this research recognizes the need to assess returns on investment in education in Central and Eastern European countries during the transition process and transition reforms, and for the first time estimates returns on investment in education during and after the transition process of the new members of the EU. This has been the main motivation for this research.
The article is structured as follows. The next section provides a theoretical basis for investigation and discusses the existing empirical literature. Detailed elaboration of the important aspects of the conducted empirical analysis that includes the data used, variables, models and methods is provided in section 3 of this article. Discussion of the results and conclusion are provided in sections 4 and 5 respectively.
From the aspect of theoretical framework, three directions have been identified to
deal with the analysis of the relationship between education and certain aspects
oflabor market. The first deals with the analysis of the relationship between
education, employee productivity and wages. Authors such as Mincer, Schultz and
Becker [18,
21, 22] viewed education as part
of Human Capital Theory by analyzing the contribution of education to productivity
growth and earnings. Mincer [18] analyzed the
returns on investment in education through wage growth, while Heckman [23]analyzed wage adjustments due to existing
(different) characteristics of work. Other authors have also investigated different
levels of earnings relative to completed education [23-28]. In literature, this is
known as the returns on investment in education analysis. The authors grouped in the
second direction analyzed the connection between education and employment
opportunities. McKenna [29] showed that
education increases employment opportunities and that educated workers are more
productive than less educated ones, whose level of productivity is limited to
specific jobs. Devine [30] showed that
education reduces the possibility of the occurrence and duration of unemployment,
while Kodde [31] showed that unemployment
increases the demand for education. Lastly, the third direction in theory deals with
the analysis of the problem of mismatch of education and labor market skills and the
existence of asymmetric information on labor market, which leads to sending wrong
signals to companies and individuals looking for employment. Within this group,
authors have analyzed phenomena such as migration and brain drain [32], overqualification [33], and lack of education [34]. Authors such as Nickell, Layard [35-38] and Calmfors [39] are also included in this theoretical
direction, whose research is focused on the effectiveness of active labor market
policies in reducing unemployment.
In case of investment in education, costs and benefits can be analyzed on the same
principle as when investing in some other sectors, projects, etc. According to
Mincer [18], private benefits are measured by
individual earnings. The easiest way to calculate private returns on investment in
education is to monitor individual wages in labor market. Wages or salaries are the
value paid for normal working hours that includes the basic wage, living expenses
and other guarantees and payments. It does not include overtime work, bonuses,
family allowances and other social benefits and payments that the employer pays
directly to employees [40]. Thus [41] showed that one additional year of
completed formal education brings an increase in earnings of 6 to 10 %. A positive
effect of investing in education at the individual level has been demonstrated in
all the leading studies [18, 42]. In calculating private returns on
investment in education, only opportunity costs are observed in most cases, while in
estimations of public returns on investment in education, social costs imply
government spending on education. However, benefits for society are also
non-monetary (non-financial) benefits for individuals, the so-called externalities
of education that include benefits such as improving health, social mobility,
reducing inequality, etc. which have been most often cited [43,44,45].
Research often shows different and even contradictory results when it comes to all
education levels and private and social rates of returns on investment in education.
Estimates by authors such as Jenkins, Bjorklund, Acemoglu, Ciccone, Doughtery and
Daly [46-52] dealt with estimates of returns on investment in education in OECD
countries, then the United States, the United Kingdom, Sweden, etc. However,
literature dealing with estimates of returns on investment in education in
developing countries has been less available. Kara [53] focused on the return on investment at three levels of education in
Turkey, Qian [54] estimated the return on
investment in education in China, [55] in
South Africa and [56] in Colombia. One of the
factors that has led to a relatively small number of studies on the return on
investment in education in developing countries has certainly been a limited
availability of data needed for quantitative analyses. There is an even greater gap
in the available scientific literature in terms of estimates of returns on
investment in education in EU Member States, especially in the new Member States
during the transition process. Fleisher [57]
included 39 studies and 11 countries in the their meta – analysis. The authors
concluded that the speed of economic transformation (speed of the reform process)
and the degree of economic imbalance measured by economic volatility are key factors
in explaining the differences in rates of returns on investment in education in the
analyzed countries. Flabbi [58] estimated the
returns on investment in education for eight countries in transition for the period
from the beginning of the transition process (1992 for all countries), with the
exception of Hungary where returns are estimated from 1986 to 2002. The authors
concluded that the evidence for the existence of a significant increase in returns
in transition countries during the analyzed period was weak. The gap in scientific
literature regarding the comprehensive analysis of returns on investment in
education in these countries, using a unique methodology in its assessment as well
as the need for this type of research, is the key argument for justifying the
research in this article. Additionally, this article bridges the gap by using the
identical methodology and data from the same databases to answer the research
questions.
A dominant methodology for estimating the return on investment in education is the
one based on the Mincer equation, noting that in scientific literature there are
different methods for estimating parameters and variations in the use of different
variables in estimation. The use of the least squares method is visible in the work
of [8] Harmon et al. (2003); Quantile
regressions in Harmon [8] and Fitzenberger
[59]; Instrumental variables in [60] and [61] and Heckman’s two-stage model in [62]. Also, the analysis showed the existence of significant differences
and contradictory conclusions regarding the estimates of return on investment in
education. Psacharopoulos [28] also stated
that there had been attempts to determine patterns of return on investment in
education, but that this had proved impossible because studies used different
models, patterns, and coverage, making them further incomparable. One of the most
significant contradictions is found in the works of Carnoy and Psacharopoulos.
Carnoy [5-6, 63] based on the analyses of
the United States and Korea, state that rates of return on investment in primary,
secondary, and tertiary education increase with the country’s level of development.
Ryoo [64; p. 71] point out that returns on
investment in lower levels of education may fall faster than returns on investment
in higher levels of education, especially in periods of rapid growth and
industrialization. This could further mean that, for example, investing in tertiary
education will have a greater impact on growth when a country reaches higher levels
of development. Unlike Carnoy, [2,24-28]
conclude that rates of return on investment in primary education are always the
highest regardless of the level of development the country is at. Thus, rates of
return on investment in education fall with the growth of the country’s level of
development. Jain and Curtin [65-66] disagree with Psacharopoulos’s theses and
argue that concentrating investment in primary education would only further increase
inequality and poverty. Likewise, the study on returns on investment in education in
China by Zhang [67] shows that returns on
investment in secondary education and higher levels of education are higher than
returns on investment in primary education. The same conclusions were reached by
Amaghionyeodiwe [68] in their analysis of the
return on investment in education in Nigeria, and earlier by Gibson [69] in the case of Papua New Guinea. On the
other hand, we find scientific research that proves Psacharopoulos’s thesis that the
returns on investment in primary education are always the highest no matter what
level of development the country is at. Works of Schutz [70] on the example of Thailand, Hossain [71] on the example of China and Sakellariou [72] on the example of Singapore show the
greatest returns on investment in primary education. In addition to the
aforementioned dominant view of returns on investment in education, we also
encounter a group of authors whose conclusions are in the middle, that is, they have
elements of both Carnoy’s and Psacharapoulos’s ideas. Trostel [73] concludes that the rate of return on investment in
education during the first years is almost zero until it begins to grow rapidly
until the age of 12 when it falls again. Heckman [23] also came to similar conclusions considering that returns on
investment in secondary education are much higher than those in primary and tertiary
education. Individual country studies such as the analysis of returns on investment
in education in Sweden [48], Turkey [53] and Colombia [56] also present similar conclusions. The analysis of the
existing scientific literature provides insight into another important feature of
research on the impact of human capital on economic growth, and that is the analysis
of the effects of different levels of education (primary, secondary, tertiary) on
economic growth. However, regardless of the level of education observed and the
variables used, science is unique in the view that the development of human capital
is a prerequisite for economic progress and that this should be taken into account
at all stages of defining development and other policies. The gap in the scientific
literature regarding the comprehensive analysis and estimation of returns on
investment in education in selected countries and using a unique methodology in
their assessment are key arguments for justifying the research focus of this
article.
DATA SOURCES
The core database used is ISSP. Authors such as Walker [74 and Trostel [73, 75]
used the ISSP [76] database for the purpose of estimating the return on
investment in education. Sampling methods vary from country to country and change
from year to year. Methods vary and some countries use a simple random sample while
other countries apply systematic sample selection, namely a stratified random
sample.This article also employs data from the World Bank's Development Indicators
database and the EUROSTAT.
STATISTICAL ANALYSIS
We use the Earnings function and the Short-cut method, both based on the Mincer equation [18] to estimate the return on investment in education in selected EU member states. However, as proposed by Psacharopoulos [24], we employ the Extended Mincer equation or the Earnings Function which includes dummy variables PRIM (primary education), SEC (secondary education) and TER (tertiary education) for the three education levels. By including different levels of education in the Mincer equation, the assumption of equal returns on investment in education for all levels of education is avoided. The dependent variable ln denotes the logarithmic value of an individual’s earnings j in time or year i and as such allows the analyses of the percentage change and the impact of independent variables in the model. The variable Sij from the basic Mincer equation (represents the total number of years an individual j has spent in formal education) has been replaced by three dummy variables denoting the three levels of education. The variable Expij denotes the years of work experience of an individual j in time (year) i, i.e. it enables the inclusion of the labor market segment in the analysis. The value of this variable is obtained by subtracting from the number of years spent in formal education and the number of years (age) at the beginning of education. Finally, the variable Exp2ij represents the squared value of the variable Expij (years of work experience). Accordingly, we specify the following models to be estimated:
Equation (1) also contains additional dummy variables: GENDER - dummy variable denoting gender; MARRIAGE - dummy variable indicating marital status and the variable YR which indicates the year of the research, i.e. the year for which the return on investment in education is assessed. According to the international classification of education - ISCED (International Standard Classification of Education), each level of education is assigned an appropriate number of years of formal education. Private returns on investment in primary education (r1) are calculated using the following equation:
r1 (primary education vs.illiterate)=β1/Sp, (2)
where β1 is the regression coefficient with the variable PRIM and Sp is the number of years of education for the level of primary education. Private returns on investment in secondary education (r2) are calculated using the following equation:
where β2 is the regression coefficient with the variable SEC, β1 is the regression coefficient with the variable PRIM, Ss is the number of years of education for the level of secondary education, and Sp is the number of years of education for the level of primary education. Ultimately, private returns on investment in tertiary education are calculated as follows:
where β3 is the regression coefficient with the variable TER, β2 is the regression coefficient with the variable SEC, St is the number of years of education for the tertiary education level and Ss is the number of years of education for the secondary education level. In order to estimate the regression coefficients with three variables of interest (PRIM, SEC and TER), a multiple regression analysis was performed as defined by equation (1). As one of the arguments in favor of using the Mincer extended equation, [26] cites a problem that may arise when estimating returns on investment in primary education. He states that there is a significant asymmetry when estimating returns for this level of education. Namely, students attending primary education (in most cases 6 to 12 years of age) are not able to earn during the same period, so it is wrong to calculate opportunity costs (as lost earnings) for the entire period. Further, Dougherty [77] and Psacharopoulos [26] state that this problem is most effectively solved by using the extended Mincer equation because in that case a shorter period of time can be assigned to opportunity costs.
The Short-cut method is considered to be a simpler form of the Earnings function because the estimation using this method requires the average wage earned in labor market with a certain level of education in the year i [18]. In the Short-cut method for estimating the return on investment in education, private returns on investment in education in the year i are calculated as follows [18, 24]:
The variable (private) from equation (5) refers to private returns on investment in education for the level of education k, where k = 1 for primary education, k = 2 for secondary education and k = 3 for tertiary education in time (year) i. The variable refers to the average earnings of an individual with a completed k level of education in a year i while the variable is the average earnings of an individual with a completed first lowest level of education. Ultimately, the variable ∆S is the difference in years of education between k and the first lowest level of education and the variable is the number of years of education corresponding to the level of education k. One of the disadvantages of this method is the absence of a variable that includes years of work experience (Exp).
The Short-cut method also allows calculation of social returns on investment in education in the year i, as follows:
In the equation (6), represents the average earnings of an individual with completed k level of education in the year i, Sk is the number of years of education corresponding to the level of education k, ∆S is the difference in years of education between ki of the first lowest level of education, while Gki represents government spending per pupil for the level of education per year i. Social returns on investment in education in the Short-cut method are estimated by additional inclusion of government spending ) for the k level of education. Using both methods, returns on investment in primary, secondary and tertiary education are estimated.
REGRESION ANALYSIS
Our methodological approach is multiple regression analysis based on the Mincer earnings function and Short-cut method as defined in equation (1) and equations (5, 6) respecively. The obtained regression coefficients are shown in table below.
Unstandardized Coefficients
Standardized Coefficients
t
Significance
Unstandardized partial regression
coefficient (B)
Std. Error
Beta
New members EU
Constant
–204,7
0,614
–333,617
0
PRIM
0,21
0,015
0,088
13,716
0
SEC
0,458
0,016
0,17
28,472
0
TER
0,724
0,017
0,18
41,951
0
Exp
0,01
0,001
0,15
19,595
0
Exp2
0
0
–0,241
–30,932
0
Gender
0,275
0,005
0,12
60,054
0
Marriage
0,042
0,005
0,018
8,472
0
YR
0,104
0
0,693
338,306
0
Old members EU
Constant
–49,349
0,407
–121,313
0
PRIM
0,559
0,008
0,338
69,667
0
SEC
0,937
0,009
0,503
109,633
0
TER
1,2
0,009
0,527
133,025
0
Exp
0,031
0
0,675
96,766
0
Exp2
0
0
–0,656
–95,425
0
Gender
0,357
0,003
0,216
115,225
0
Marriage
0,042
0,003
0,025
12,624
0
YR
0,027
0
0,256
132,012
0
According to the results of the regression analysis, each additional year of work experience in the group of new members of the European Union brings an increase of about 1 % of salary, while in the group of old members it is 3,1 %. Men in the new EU member states earn on average about 27 % more than women, while the difference in the group of old members is about 37 %.
ESTIMATES BASED ON THE EARNINGS FUNCTION
PRIM, SEC and TER from Table 2 are the results of regression estimates with the above three variables and are needed to calculate private returns on investment in primary (r1), secondary (r2) and tertiary education (r3).
PRIM
SEC
TER
r1
r2
r3
Old EU
members
0,559
0,937
1,200
9,3
6,3
6,6
New EU
members
0,21
0,458
0,724
3,5
4,1
6,6
The largest gap between the old and new members of the EU, according to the estimates
obtained using the Earnings Function, is visible at the level of primary education
and it decreases when the level of education increases. The results of the estimated
returns on investment in education in the group of old members are in line with the
thesis of Psacharopoulos [26] who considers
that returns on investment in education are highest at the level of primary
education regardless of the level of development of the country. The pattern of
return on investment in education in the old EU member states is in line with a
previous research by Hartog [78] who points
out the existence of the highest returns on investment in primary education,
followed by a decline in returns on investment in secondary education and a slight
increase again in tertiary education. The authors call this a U-shaped pattern.
However, the estimated returns on investment in education in the group of new EU
members show a different pattern. Namely, the returns in this group are more in line
with previous research by Zhang, Amaghionyeodiwe and Gibson [67-69], which pointed
out the existence of the highest returns from investing in secondary and tertiary
education. The ideas of Psacharopoulos [25-26] are partly applicable to
both groups of countries analyzed, at least as far as the pattern of return on
investment in education is concerned. Namely, the author believes that it may come
from a slight increase in return on investment in education in the transition from
secondary to tertiary education. The average return on investment in education in
the group of old members is 7,4 %, which is close to the OECD average (7,5 %)
according to Psacharopoulos [28], while the
average return on investment in education in the group of new EU members was lower
than the estimated returns in the group of old members and amounted to 4,7 %.
Country
PRIM
SEC
TER
r1
r2
r3
Austria
–0,097
0,219
0,269
–1,6
5,3
1,3
Belgium
0,187
0,456
0,743
3,1
4,5
7,2
Denmark
–0,09
0,126
0,37
–1,5
3,6
6,1
Finland
–0,077
0,212
0,479
–1,3
4,8
6,7
France
0,226
0,55
0,878
3,8
5,4
8,2
Germany
0,19
0,473
0,781
3,2
4,7
7,7
Ireland
–0,027
0,289
0,602
–0,4
5,3
7,8
Italy
–0,169
0,224
0,466
–2,8
6,6
6
Netherlands
0,161
0,336
0,556
2,7
2,9
5,5
Portugal
0,36
0,78
1,193
6
7
10,3
Spain
0,162
0,451
0,754
2,7
4,8
7,6
Sweden
0,006
0,182
0,404
1
2,9
5,6
United Kingdom
–0,592
0,3
0,592
–9,9
14,9
7,3
Private returns estimated by the earnings function in this group of countries were
highest at the level of tertiary education in all countries except the United
Kingdom, Austria and Italy as seen in Table 3. Earlier estimates of returns on
investment in education in the United Kingdom have shown significant differences
when compared to other countries. Namely, [23] estimated returns on investment in education for the United Kingdom
between 7 % - 9 % (OLS estimates) and 11 % - 15 % (estimates using instrumental
variables), while estimates for other countries averaged around 6 % (OLS estimates)
and 9 % (estimated using instrumental variables). Walker [74] also point to relatively higher estimates of return on
investment in education in the United Kingdom (8 % –10 %) than the average of other
countries (6,5 %). Table 3 also shows negative returns on investment in education,
exclusively at the level of primary education. According to the authors Henderson
[79] and Trostel [73], the occurrence of negative returns on investment in
education is not uncommon as they are the same indicators of the existence of low
returns for a certain level of education. The same applies to the results presented
in Table 4.
Country
PRIM
SEC
TER
r1
r2
r3
Bulgaria
0,077
0,345
0,484
1,3
4,5
3,5
Croatia
0,094
0,367
0,66
1,6
4,6
7,3
Cyprus
–0,186
0,429
0,481
–3,1
10,3
1,3
Czech
Republic
–0,07
0,098
0,319
–1,2
2,8
5,5
Estonia
–0,104
0,053
0,365
–1,7
2,6
7,8
Hungary
0,282
0,575
0,849
4,7
4,9
6,9
Latvia
–0,09
0,175
0,365
–1,5
4,4
4,7
Lithuania
–0,129
0,1
0,368
–2,2
3,8
6,7
Poland
0,028
0,221
0,655
0,5
3,2
10,9
Slovakia
0,152
0,364
0,665
2,5
3,5
7,5
Slovenia
0,047
0,45
0,731
0,8
6,7
7,0
The highest private returns on investment in tertiary education were recorded in all
countries except in two cases: Bulgaria and Cyprus, where the highest returns on
investment in secondary education were estimated. Estimated returns on investment in
education in the new members of the Union show the highest average returns on
investment in Hungary, Poland and Slovenia (above the average of the new members).
These results are in line with the results of individual studies of these countries,
namely: a growth in return on investment in education in Hungary during 1989-1996.
[80]; a growth in return on investment in
education in Poland during 1992-1995 [81] and
a growth of returns on investment in education in Slovenia during 1993 [82]. Flabbi [58] cite the example of Hungary and Poland as the countries with the
highest returns on investment in education among the eight transition countries
analyzed. The authors cite structural reforms and institutional frameworks as
possible explanations for higher returns in Hungary and Poland, specifically citing
a planned and structured education reform during the transition process as an
important success factor. The estimate also shows the lowest average returns on
investment in education (including old and new members of the European Union) in
Austria (1,7 %). Authors Fersterer et al. [83] emphasize the existence of low returns on investment in education
in Austria, citing the period 1981–1997 as the period of the largest decline in
return on investment in education. Low average returns on investment in education in
the group of old EU member states were also recorded in Sweden. Bjorklund [48] analyzes the decline in returns on
investment in education in Sweden.
ESTIMATES BASED ON SHORT-CUT METHOD
Estimated private returns on investment in primary, secondary and tertiary education in the new EU member states by the Short-cut method are highest at the tertiary education level, as is the case with the Earnings Function-based estimate.
r1(private)
r2(private)
r3(private)
Old EU
members
7,8
6,3
7,8
New EU
members
8,1
6,3
7,7
However, the estimation of private returns using the Short-cut method showed significantly higher returns in all countries, and especially higher returns at the level of primary education (Bulgaria, Croatia, Cyprus, Latvia, Slovakia). Nevertheless, the highest average returns estimated by the Short-cut method as is the case with the Earnings Function are in Hungary and Poland. Also, the estimates based on the Earnings Function show negative returns on investment, which are almost non-existent in Short-cut method estimates (except in the case of Austria, Denmark and Estonia).
During the period from the beginning of the transition process of the new EU members,
the average private return on investment in primary education was about 7 %, while
estimating the return based on the Earnings Function we get an average return of 3,5
% for the same level education. And Sosic [84] emphasizes the importance of secondary education with special
emphasis on vocational education in centrally planned economies, stating that
significant financial resources have been invested in this level of education.
Namely, planned production, which in a large number of cases referred to the
exploitation of natural resources or their simple processing, required knowledge and
skills at the level of primary and / or secondary vocational education. Additional
justification for low returns from investment in primary and secondary education in
the new member states of the Union, and in the above context, is in a significant
share of persons with completed primary and secondary education in the structure of
employees in the pre-transition period. These conclusions are consistent with the
earlier conclusions of Card [85] and Flabbi
[58] who also point out the growth of
returns in the first stages of transition and the absence of significant growth in
the later stages of the process. The increase in returns on investment in education
since the beginning of the transition is linked to the first phase of education
reforms, which, according to OECD [86-90], saw a significant inflow of foreign
funding. However, there was no significant increase in return on investment in
education during the same time period in the old member states. Walker [74] state that there is a global trend of
declining returns on investment at all levels of education during the 1990s and
especially in the second half of the decade. Fleisher [57] recorded an increase in average private returns on
investment in education in transition countries from about 5 % (1989) to about 8 %
(1990s). The evaluation by the Short-cut method showed identical results for the new
members of the Union during these two periods. It also showed that average returns
on investment during the 2000s remained at the same level as during the 1990s (8 %).
EBRD [91] states that transition countries
still face the problem of lack of skilled labor, which, according to Rutkowski [81], has led to a certain increase in returns
which, according to the same author, has slowed down and is expected to stagnate and
eventually decline. The estimated average returns on investment in education in the
old member states of the Union began to fall in the 2000s compared to the 1990s.
From a theoretical point of view, Becker [22]
states that the decline in return on investment in education comes with an increase
in the share of the population with a high level of education, or generally due to
an increase in the level of education of the workforce. This is also known in
literature as the Becker’s Woytinsky Lecture Hypothesis. Psacharopoulos and Walker
[27,
74] also state the same reason for the decline in returns on investment
in education.
Estimation of return on investment in education with the Short-cut method shows that
in both groups of countries returns on investment in primary education were the
highest, which is consistent with Psacharopoulos [2,
24-27]who believes that returns on investment in education are always the
highest for primary education no matter what level of development the country is at.
Also, the results of estimates of returns on investment in education using the
Short-cut method show the largest decline in returns on investment in tertiary
education during the period of initial transition reforms in the new member states
(1988-1993) in contrast to Ryoo [64], who
emphasize the existence of a faster decline in returns from investing in lower
rather than in higher levels of education.
In the group of old EU member states, the highest social returns on investment were
recorded at the level of tertiary education, except in the case of Austria, where
the highest social returns on investment in secondary education were recorded.
Authors Fersterer et al. [83] cite an
increase in the supply of highly educated labor as one of the reasons for the
decline in the return on investment in education in estimating the return on
investment in education in Austria during the period 1981-1997. Henderson [79], as previously stated in the article,
consider that negative returns on investment are an indicator of very low returns on
investment in education.
Primary
education
Secondary
education
Tertiary
education
Old members
EU
6,4
9,7
15,2
New members
EU
6,5
9,9
17,1
If we look at the period before the transition of the new members (1985-1990), the first phase of transition (1991-1990) and the second phase of transition and integration (2000-2014), the average social returns on investment in education are given in the table below:
1986. –
1990.
1991. –
1999.
2000. –
2014.
Old members
EU
7,0
8,0
7,0
New members
EU
5,0
8,0
8,0
Average social returns on investment in education in the old members of the Union indicate a decline during the 2000s, while average social returns on investment in education increased in the new members of the Union over the same time period. Namely, the old members of the European Union after 2001 also recorded a decline in investment in education, especially in tertiary education (a decline of 6 % of GDP in 2005 compared to the level of investment in 1998).
Investment in
primary education
Investment in
secondary education
Investment in
tertiary education
Old members
EU
19,8
25,8
34,4
New members
EU
19,2
22,4
26,7
The difference in investment at the tertiary education level is the biggest.
According to Psacharopoulos, Mingat and World Bank [28, 92-93], primary education is more socially profitable in
low-income countries while secondary and tertiary education are more socially
profitable in middle- and high-income countries. Mingat [92] showed that social returns from investment in tertiary
education are highest in the case of developed countries (20 %) and are low or even
negative at the level of secondary education because the benefits of secondary
education coverage are small in comparison to costs. However, according to
Psacharopoulos [26], social returns from
investing in secondary education are highest.
NONLINEARITY IN RETURNS ON INVESTMENT IN EDUCATION
The research results in this article are partly in line with earlier findings by
Heckman and Trostel [23, 73,75] which suggested
nonlinearity in returns on investment in education. Namely, the linearity in returns
would mean an increase in return on investment in education with an increase in the
level of education. Authors such as Heckman and Trostel [23, 73,75] also point out the existence of
nonlinearity in terms of the increase in return on investment in secondary education
relative to primary education and the decrease in return on investment in tertiary
education relative to secondary education. However, the pattern of nonlinearity in
the scientific literature that deals with returns on investment in education is
unclear. Trostel [73] argues that endogeneity
in education may partly explain the increasing returns from investment in education
from the beginning of the education process, but not the declining returns from the
increasing levels of education.
The estimated returns on investment based on the Earnings Function and Short-cut method in this article show nonlinearity, as is the case with previous research [7375]. Nonlinearity occurs, among others due to:
• The assessment of return on investment in education considers only the quantity of education through the number of years spent in education without considering the quality of education and equal access to quality education for all participants in the education process.
• The assessment of the return on investment in education does not consider the
specifics of national labor markets, which primarily refers to the relationship
between the level of wages and labor productivity. Barro [9] emphasizes the existence of higher wages relative to the
level of productivity in the public sector compared to the private sector, which can
ultimately lead to the emergence of nonlinearity.
• The mismatch between the education system and the needs of labor market leads to a shortage or surplus of labor with certain knowledge and skills, which significantly affects the return on investment in education [26].
The most significant difference in terms of returns at different levels of education
between the old and new members in the research exists at the level of tertiary
education in the pre-transition period and the period from the beginning of the
transition process. Namely, this is a period during which the new members were still
predominantly centrally planned economies or were at the very beginning of the
transition process, where market forces of supply and demand for certain skills did
not affect the level of wages in labor market. World Bank [93] states that wage inequality was much lower during the
transition process compared to the then OECD average. The increase in returns on
investment in tertiary education in the new member states was recorded after 2007,
where returns averaged 8 %. Returns from tertiary education in this group of
countries during 1995–2014 ranged between 6 %–10 %, which according to
Psacharopoulos and Acemoglu [26, 28,
49]] and is the average for high-income countries and OECD countries.
Gibson [69] also highlights the existence of
a convergence of returns on investment in education in transition countries to a
world average of 10 %. Psacharopoulos [26, 28] also concludes that the
general picture of the return on investment in education is in fact based on the law
of declining returns despite the slight increase in returns relative to the level of
development of the country. If we analyze the return on investment by the level of
education estimated by the Short-cut method, we come to the following conclusions
about possible trends and patterns:
• Return on investment in primary education in the new EU member states follows the trends in the old member states.
• The returns on investment in tertiary education in the old members of the Union in
the second half of the 1980s were higher than the returns in the new members. The
beginning of the transition (1990-1994) is described as a period marked by
instability, but after that there comes an increase in returns on investment in
tertiary education. However, as stated by Flabbi [58], the evidence for the existence of increasing returns on investment
in education is weak.
However, according to some authors, instabilities in terms of return on investment in
education in the new member states of the Union during the transition process were
not great given the number and importance of reforms. Fleisher [57] offered an explanation based on reforms that focused on
the liberalization of legislation, in particular labor legislation as well as other
institutional constraints related to the regulation of wage levels in labor market.
The faster the reforms were implemented, the faster the returns on investment
adjusted to the market. Hung [94] states that
the countries in which the so-called Shock therapy (Bulgaria, Slovakia, and the
Czech Republic) had higher average returns than investments in education in
comparison to other countries. Also, other successful transition countries (Hungary,
Poland) have returns on investment in education that are above average, as estimated
in the research in this doctoral dissertation. Schultz [15] emphasizes the importance of structural transformations by
stating that more educated individuals are able to better adapt and respond to new
challenges and opportunities, which alleviates initial instabilities in labor
market. Finally, Chase [95] and Flanagan
[96] highlight a change in the value
system in these new economies that took education into account when defining wages
in labor market. The analysis of estimated returns on investment in primary,
secondary and tertiary education using The Short-cut method shows an overall slight
decline in returns on investment in education in all countries, and this is in line
with the conclusions of [8,26,
28, 73, 75,97] regarding global
trends in returns on investment in education.
NEGATIVE RETURNS ON INVESTMENT IN EDUCATION AND LESSON FOR SUSTAINABLE EDUCATION POLICY
Negative returns on investment in education are not uncommon and modern scientific
literature especially in cases where returns are estimated in developed countries. A
situation where there is a disparity in knowledge and skills in scientific
literature is known as thecase of overqualification and / or insufficient
qualification of the workforce (persons accept employment where their level of
knowledge and skills does not match job description and job tasks) [98]. In literature, we find more detailed
analyses related to the cases of supply-side problems (supply increase). Chevalier
[99] analyzed the case of the United
Kingdom, stating that the increase in student enrollment in the mid-1980s, together
with lower costs per student, led to a decline in returns on investment in
education. Freeman [100] states that in the
case of the United States during the 1970s, there was a decline in returns on
investment in tertiary education due to an increase in the supply of university
graduates. Authors such as Dutta [101] also
state that a similar phenomenon is possible in developed countries during financial
instabilities and crises when there is an increase in supply due to a falling
employment and ultimately a fall in return on investment in education. Chevalier
[99] further states that the patterns of
supply and demand in labor markets of developed countries have shown over the last
forty years that there is a trend of insufficient qualifications at the beginning of
the transition process and that after that comes a period of retraining as the
transition process finalizes. Share of highly educated people in the total workforce
has increased significantly in recent decades. Authors such as Henderson, Chevalier,
Dolton and Moffitt [79,99, 102, 103] state that this is especially
significant in the case of developed countries where investment returns fall and
there is an increase in the level of education of the total population. The return
on investment in education is reported to fall between 5 %-26 % in the event of
retraining in developed countries [98,99,
102, 104]. In addition to the
stated (possible) reasons for the mismatch between supply and demand in labor
market, which can be stated to be temporary, Chevalier [99] argues that retraining is the result of a serious
imbalance and as such may be permanent.The author also states that retrained
individuals continue to be retrained for a given job over time and that retraining
is the result of an inefficient resource allocation and also brings cost to both the
individual and the society. As the only solution, the author cites a more efficient
allocation of available resources. One of the basic policies that appears as a
possible solution to the problem of negative returns on investment in education is
labor legislation or a legal framework that will regulate labor market and thus
significantly affect the return on investment in education. This applies in
particular to legal provisions regarding the minimum wage that most directly affects
supply and demand in labor market. However, as such, it can create a picture of
non-existence of the need for education in society and ultimately lead to a decline
in the return on investment in education [105,106] cite Russia and other
(communist) countries as cases where there are negative returns on investment in
education resulting from government intervention or some other (non-market)
compensation and compensation for work
Investing in education is significant, whether it is investing in inputs or outputs
of education, or in the process itself. However,contemporary literature that deals
with returns on investment in education,shows discrepancies in terms of conclusions,
as well as recommendations and practical implications of research. We see a key
difference in the reflections of authors Carnoy and Psacharopoulos. Namely, Carnoy
[5,6, 63, 107] believes that returns on investment in education depend
on the level of development of the country, while Psacharopoulos [2,24-27] considers that returns on
investment in primary education are always highest regardless of the level of
development of the country. Estimated returns on investment in education in the EU
member states indicate the existence of a gap between the old and new members of the
Union and the absence of a significant increase in the return on investment in
education. Using the Earnings function, we can see that estimated average private
returns on investment in education in the new EU member states are lowest at the
level of primary education. They increase with the increaseof the level of
education, which makes returns linear, while the results of the cutting method show
nonlinearity, i.e. a U-curve. This research confirms the theses of Krueger [108 and Amaghionyeodiwe [68], that different levels of return on investment in
education are the result of using different methodologies in assessment, although
this research went a step further by focusing on assessing returns by using data
from the same (consistent) source. Psacharopoulos [2,24-27] believes that returns are always highest for the level of
primary education no matter what level of development the country is at. However,
this article uses identical methods which lead to contradictory assessment results.
Ultimately, one of the research objectives of this article is to contribute to
scientific literature and to contemporary methodological discussions regarding the
methodology for estimating the return on investment in education. Authors such as
Menon [109-110] have shown that Elaborate method and Short-cut method are
interchangeable, and that both methods indicate significant heterogeneities between
groups of countries and between countries separately.
This article, although covering a relatively long period of time (1985–2014), did not show homogeneous patterns of return on education investment in these two groups of countries but rather indicated significant heterogeneities among the countries themselves. Therefore, it is necessary to interpret the relationship between wages and levels of education for each country separately, taking into account its specifics such as the relationship between education policy and other policies (e.g. labor market policy and budgetary policies) and national reforms (whether transitional reforms or integration process), and taking into account regional and global trends in education, such as the growing importance of international organizations, regional projects, programs, etc. Another potential limitation of work is the data used in estimating the return on investment in education. Namely, estimating the return on investment in education using methods based on the Mincer equation requires data at the micro level, which in most cases are collected through differently designed questionnaires and collection methods in general. All research created in this way has its own limitations. Taking into account the declining returns on investment in tertiary education as well as the significant expansion of this level of education across EU countries, it is necessary to further analyze specific policies that could be used to overcome labor market mismatches such as identifying active policies and other policies that will stimulate a higher level of labor market flexibility.
LevinR.M.. .
New York: Teachers College Press,
1970.
PsacharopoulosG.. .
Amsterdam: Jossey-Bass, Elsevier,
1973.
NormanD.V.. .
Oslo: Universitetsforlaget,
1976.
CohnE.. .
New York: Pergamon Press,
1990.
CarnoyM.. .
New York: Pergamon Press,
1995a.
CarnoyM.. .
New York: Pergamon Press,
1995b.
JohnesG.. .
New York: St. Martin's Press,
1993.
HarmonC.2003. The returns to education: Microeconomics.
,
17,
(2)115-155. 10.1111/1467-6419.00191Barro, J.R. and Lee, J-W.:
A New Data Set of Educational Attainment in the World, 1950-2010.
(2010)
.
http://dx.doi.org/10.3386/w15902LucasE.R.1988. On the mechanics of economic development.
,
22,
(1)3-42. 10.1016/0304-3932(88)90168-7RayD.. .
Princeton, New Jersey: Princeton University Press,
1998.
BarroJ.R.. .
New York: McGraw-Hill,
1995.
BarroJ.R.. .
Cambridge: The MIT Press,
1997.
BeckerS.G.. .
New York: NBER,
1964.
SchultzW.T.. . The Free Press, 1971. MincerJ.1958. Investment in human capital and personal income distribution.
,
66,
(4)281-302. 10.1086/258055MincerJ.1962. On the job training costs, returns and some implications.
,
70,
(5)50-79. 10.1086/258725MincerJ.. .
New York: NBER Press,
1974.
BeckerG.1960. Underinvestment in college education.
,
50,
(2)346-354. 10.1016/0002-9394(60)90031-3BeckerS.G.. .
Chicago: The University of Chicago Press,
1993.
Schultz W.T.1961. Investment in human capital.
,
1,
(2) 1-17. 10.1017/S0027950100018378BeckerS.G.1962. Investment in human capital: A Theoretical Analysis>.
,
70,
(5)9-49. 10.1086/258724HeckmanJ.J.2008. Earnings function and rates of return.
,
2,
(1)1-31. 10.3386/w13780PsacharopoulosG.1981. Returns to Education: An Updated International Comparison.
,
17,
(3)321-341. 10.1080/0305006810170308PsacharopoulosG.1985. Returns to Education: A Further International Update and
Implications. ,
20, (4)583. 10.2307/145686PsacharopoulosG.1994. Returns to Investment in Education: A Global Update.
,
22,
(9)1325-1343. 10.1016/0305-750X(94)90007-8Psacharopoulos, G..
Returns to investment in higher education. A European Survey.
(2009)
PsacharopoulosG.2004. Returns to investment in education: a further update.
,
12,
(2)11-134. 10.1080/0964529042000239140McKennaC.J.1996. Education and the distribution of unemployment..
,
12,
(1)113-132. 10.1016/0176-2680(95)00040-2DevineJ.T.. .
Oxford: Oxford University Press,
1991.
KoddeA.D.1988. Unemployment expectations and human capital formation.
,
32,
(8)1645-1660. 10.1016/0014-2921(88)90023-2StarO.1998. Human Capital Formation, Asymmetric Information, and the Dynamics of International Migration.
,
52.
SloaneJ.P.. .
Cheltenam: Edgar Elgar,
2003.
SloaneP.1999. Overeducation, undereducation and the British labour market.
,
31,
(11)1437-1453. 10.1080/000368499323319NickellS.1997. Unemployment and labour market rigidities: Europe versus North America.
,
11,
(3)55-74. 10.1257/jep.11.3.55NickellS.. .
Amsterdam: North Holland,
1999.
NickellS.2002. Unemployment in the OECD since the 1960s; what do we know? .
,
115,
(500)1-27. 10.1023/A:1017414823878LayardR.. .
Oxford: Oxford University Press,
1991.
CalmforsL.1994. Active labour market policy and unemployment: A
framework for the analysis of crucial design features.
, 22,
4-47. ILO Wage.
Wages.
(2017)
http://www.ilo.org/global/topics/wages/lang--en/index.htmDuflo2001. Schooling and labor market consequences of school construction in Indonesia: Evidence from an unsual policy experiment.
,
91,
(4)795-813. 10.1257/aer.91.4.795PatrinosH.A.. .
San Diego: Elsevier,
2010.
OECD.
Understanding the Social Outcomes of Learning.
(2007)
.
10.1787/9789264034181-enhttp://dx.doi.org/10.1787/9789264034181-enLimD.. .
Brookfield: Edward Elgar,
1996.
McMahonW.W.. .
Oxford: Oxford University Press,
2002.
JenkinsP.S.1995a. Did the middle class shrink during the 1980s? UK evidence from kernel density estimates.
,
49,
(4)407-413. 10.1016/0165-1765(95)00698-FJenkinsP.S.1995b. Accounting for Inequality Trends: Decomposition Analyses for the UK, 1971-86.
,
62,
(245)29-63. 10.2307/2554775BjorklundA.2002. Estimating the return to inestment in education: How useful is the standard Mincer equation?.
,
21,
(3)195-210. 10.1016/S0272-7757(01)00003-6AcemogluD.2000. How Large Are Human-Capital Externalities? Evidence from Compulsory Schooling Laws.
,
15,
9-5. 10.1086/654403CicconeA.2006. Identifying Human-Capital Externalities: Theory with
Applications. ,
73, (2)381-412. 10.1111/j.1467-937X.2006.00380.xDoughteryC.2005. Why are returns to schooling higher for women than for men?.
,
40,
(4)969-988. 10.3368/jhr.XL.4.969DalyA.2015. The private rate of return to a university degree in Australia.
,
59,
(1)97-112. 10.1177/0004944114565117KaraO.2010. Comparing two approaches to the rate of return to investment in education.
,
18,
(2)153-165. 10.1080/09645290802416486Qian2008. Private returns to investment in education: an empirical study of urban China.
,
20,
(4)483-501. 10.1080/14631370802444732Branson, N. and Leibbrandt, M.:
Educatonal Attainment and Labour Market Outcomes in South Africa, 1994-2010.
(2013)
.
No. 1022.
Garcia-SuazaA.F.2014. Beyond the Mincer equation: the internal rate of return
to higher education in Columbia. , 22, (3)328-344. 10.1080/09645292.2011.595579FleisherM.B.2005. Returns to Skills and the Speed of Reforms: Evidence from Central and Eastern Europe, China, and Russia.
,
33,
(2)351-370. 10.1016/j.jce.2005.03.003FlabbiL.2008. Returns to education in the economic transition: A systematic assessment using comparable data.
,
27,
(6)724-740. 10.1016/j.econedurev.2007.09.011FitzenbergerB.K.R.. .
New York: Physica-Verlag,
2002.
Denny, K. and Harmon, C.:
The Impact of Education and Traning in the Labour Market Experiences of Young Adults.
(2000)
.
ZamarroG.2010. Accounting for heterogeneous returns in sequential schooling decisions.
,
156,
(2)260-276. 10.1016/j.jeconom.2009.10.018Serumaga-ZakeA.E.P.2003. The use of the double-hurdle model in estimating the rate of return to education in South Africa.
,
27,
(3)103-119. 10.1080/10800379.2003.12106355CarnoyM.1975. The return to schooling in the United States, 1939-1969.
,
10,
(3)312-331. 10.2307/145194RyooJ-K.1993. Changing rates of returns to education over time: A Korean case study.
,
12,
(1)71-80. 10.1016/0272-7757(93)90044-HJainB.1991. Returns to education: Further analysis of cross country data.
,
10,
(3)253-258. 10.1016/0272-7757(91)90048-TCurtinT.R.C.1999. Economic and health efficiency of funding policy.
,
48,
(11)1599-1611. 10.1016/S0277-9536(99)00084-2ZhangQ.2007. Returns to education, productivity and economic growth in China.
,
9,
(3)293-308. 10.1080/13876980701494707AmaghionyeodiweL.A.2007. Do Higher Levels Of Schooling Lead To Higher Returns To Education In Nigeria?.
,
7,
(1)GibsonJ.2006. Subsidies, selectivity and the returns to education in urban Papua New Guinea.
,
25,
(2)133-146. 10.1016/j.econedurev.2005.01.002SchutzT.P.1993. Investment in the schooling and health of women and man.
,
28,
(4)694-734. 10.2307/146291Hossain, I.S..
Making education in China equitable and efficient.
(1997)
.
SakellariouC.2003. Rates of return to investment in formal and technical/vocational education in Singapore.
,
11,
(1)73-87. 10.1080/09645290210127525TrostelA.P.2005. Nonlinearity in the return to education.
,
8,
(1)191-202. 10.1080/15140326.2005.12040624Walker, I. and Zhu, Y.:
The Returns to Education: Evidence from the Labour Force Survey.
(2001)
.
TrostelP.2002. Estimates of the economic return to schooling for 28 countries.
,
9,
(1)1-16. 10.1016/S0927-5371(01)00052-5ISSP:
International Social Survey Database.
DoughertyR.S.C.1991. The specification of earnings functions: tests and implications.
,
10,
(2)85-98. 10.1016/0272-7757(91)90001-6HartogJ.2001. Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quantile regression estimators.
,
33,
(8)1021-1037. 10.1080/00036840122679HendersonJ.D.2011. Heterogeneity in Schooling Rates of Return.
,
30,
(6)1202-1214. 10.1016/j.econedurev.2011.05.002Kertesi, G. and Kollo, J..
Economic transformation and the revealution of human capital – Hungary, 1986-1999.
(1999)
.
No 104.
RutkowskiJ.1996. High Skills Pay – off: The Changing Wage Structure during Economic Transition in Poland.
,
4,
(1)89-112. 10.1111/j.1468-0351.1996.tb00163.xStanovnikT.1997. The Returns to Education in Slovenia.
,
16,
(4)443-49. 10.1016/S0272-7757(97)00006-XFerstererJ.2003. Are Austrian Returns to Education Falling Over Time?.
,
10,
(19)73-89. 10.1016/S0927-5371(02)00105-7SosicV.2003. Premium for education and investment in human capital in Croatia. In Croatian.
,
27,
(4)439-455.
CardD.2001. Estimating the return to schooling: Progress on some persistent econometric problems.
,
69,
(5)1127-1160. 10.3386/w7769OECD:
Estonia: Reviews of National Policies for Education.
(2001a)
.
OECD:
Latvia: Reviews of National Policies for Education.
(2001b)
.
OECD:
Lithuania: Education and Skills. Reviews of National Policies for Education.
(2002)
.
OECD:
Bulgaria, Science, Research and Technology. Reviews of National Policies for Education.
(2004)
.
OECD:
Romania, Education and Skills. Reviews of National Policies for Education.
(2000)
.
EBRD:
Transition report: Innovation in transition.
(2014)
.
Mingat, A. and Tan, J-P.:
The full social returns to education: estimates based on countries' economic growth performance.
(1996)
.
World Bank:
Enhancing job opportunities – Eastern Europe and the Former Soviet Union.
(2005)
.
HungF-S.2008. Returns to education and economic transition: an international comparison.
,
38,
(2)155-171. 10.1080/03057920701420957ChaseS.R.1998. Markets for Communist Human Capital: Returns to Education and Experience in the Czech Republic and Slovakia.
,
51,
(3)401-423. 10.1177/001979399805100303FlanaganJ.R.1998. Were Communists Good Human Capitalists? The Case of the Czech Republic.
,
5,
(2)295-312. 10.1016/S0927-5371(97)00029-8HarmonC.2001. Dispersion in the economic return to schooling.
,
10,
(2)205-214. 10.1016/S0927-5371(03)00003-4Galasi, P.:
The effect of educational mismatch on wages for 25 countries.
(2008)
.
ChevalierA.2003. Measuring over-education.
,
70,
(279)509-531. 10.1111/1468-0335.t01-1-00296FreemanB.R.. .
New York: Academic Press,
1976.
DuttaJ.1999. Education and public policy.
,
20,
(4)351-386. 10.1111/j.1475-5890.1999.tb00017.xDoltonP.2000. The incidence and effects of overeducation in the U.K. labour market.
,
19,
(2)179-198. 10.1016/S0272-7757(97)00036-8MoffittR.2007. Estimating marginal returns to higher education in the
UK. , 10.3386/w13534CohnE.1995. The wage effects of overschooling revisited.
,
2,
(1)67-76. 10.1016/0927-5371(95)80008-LFunkhouserE.1998. Changes in the returns to education in Costa Rica.
,
57,
(2)289-317. 10.1016/S0304-3878(98)00090-XBenitez-Silva, H. and Sheidvasser, S.:
The Educated Russian's Curse: Returns to Education in the Russian Federation.
(2000)
.
Stony Brook University, Department of Economics.
CarnoyM.. .
New York: Pergamon Press,
1972.
KruegerB.A.2001. Education for Growth: Why and For Whom?.
,
39,
(4)1101-1136. 10.1257/jel.39.4.1101MenonM.E.1997. Perceived rates of return to higher education in Cyprus.
,
16,
(4)425-430. 10.1016/S0272-7757(96)00065-9MenonM.E.2008. Rates of return to higher education: Further evidence from Cyprus.
,
27,
(1)39-47. 10.1016/j.econedurev.2006.07.009