Aneta Sobotka
Educational Research Institute
The Robert B. Zajonc Institute for Social Studies
University of Warsaw
The Impact of Imperfect Information
on Higher Education Decisions in Poland
1. Objectives
A characteristic feature of the higher education market is the existence of imperfect
information (Stiglitz 2000). The uncertainty affects many areas – effort and time needed for
studying, teaching quality, all costs associated with studying, chances for graduating, the
benefits of investment in education, understood as employability, and earnings (Grotkowska,
Sztanderska 2014). The presence of imperfect information in the higher education market is
also related to uncertainty of the future labour market needs and the specific nature of higher
education which is perceived to be an experience good (McPherson, Winston 1995), whose
quality is very difficult to assess before purchase. In the era of mass higher education, the key
question is not whether to attend college, but which college and which major to choose
(Hoxby 2007). The important problem is whether the prospective students and their
households have sufficient knowledge to make economically rational decisions which college
to choose (Dill, Soo 2004). In recent years, there is a significant improvement in access to
information which colleges offer the best value and which majors are most in demand. This is
associated with numerous studies and publications analysing situation of college graduates in
the labour market, monitoring graduates careers by universities and greater availability of the
Internet access.
The main objective of this study is to characterize the phenomenon of imperfect
information in the higher education market in Poland and to estimate how imperfect
information affects educational choices of people beginning studies.
The specific objectives:
1. To analyze changes in the access to information about the benefits of higher education
in the years 2000-2015.
The analysis of the existing data on educational choices of college graduates will show how
access to the information about the benefits of higher education has changed during last 15
years. The main measure will be the return rate to investment in higher education on national
and regional level, and by college major.
2. To analyze the structure of the choices made by people beginning studies in the
context of available information on unemployment by major.
This analysis will show, if there is any correlation between information on college
unemployment rate by major, and major choices structure in the next years. If it is true, is the
change immediate or rather slow? The analysis will be carried out on national, and regional
level on the chosen majors.
3. To analyze the use of information from graduate tracer studies by colleges.
In 2011 the Ministry of Education and Science introduced the obligation for higher education
institutions to monitor the graduates’ careers to better adjust the programme of studies to
labour market requirements. The analysis of reports from the monitoring will show which
information is the most often analysed by colleges, and which information is lacking.
4. To analyze prospective students information needs.
Many young people before they make a choice where and what to study, try to find some
information. This analysis will show which information is the most crucial for prospective
students, what are the most popular sources of this information, and what is lacking.
Main hypothesis:
Increased availability of information on graduates’ employment prospects affects the
structure of major choices in the next years.
Greater access to information on the costs and benefits of higher education leads to more
satisfactory educational choices on the individual level.
2. Significance and theoretical framework
According to human capital theory (Becker 1964) and the Mincers earnings function (1958),
investment in education brings wide-ranging individual and social benefits. The individuals
expect that investment in education will provide them higher earnings in the future. Benefits
from education can be broadly understood – it can be everything what is important for an
individual, e.g. better working conditions or longer life (Eide, Showalter 2006), but in
empirical studies the most common measure of the benefits is the return rate to the investment
in education. Just as any investments, investment in education entail some costs which can be
direct, such as tuition fees or indirect such as foregone earnings. Human capital theory
assumes that individuals are rational, and will invest in education as long as the return rate
will be high enough and benefits will be greater than costs. Essential to the concept of
investing in human capital is that individuals have necessary knowledge to make educational
choices (Kern 2012).
Perfect market competition assumes existing of perfect information which is very rare. The
much more often is situation where one party has more information or better quality
information than other what is called in economics information asymmetry. The theory of
information asymmetry, was created by Nobel Prize winners – George Akerlof, Joseph E.
Stiglitz and Michael Spence. The term of asymmetric information was first introduced in
1970 by Akerlof (1970), who analysed the quality uncertainty on the market of used cars.
Akerlof showed that asymmetric information leads to adverse selection and sub-optimal
market outcomes. The two ways of overcoming information asymmetry is signalling (Spence
1973) and screening (Stiglitz 1975).
In the case of higher education market, information problems better describes the term
imperfect information, because in contrast to asymmetric information, both parties (students
and colleges) are uncertain about many features of educational good, e.g. both do not know
employability perspectives. Information about the good is required for satisfactory
consumption or investment. The goods may be divided into search goods and experience
goods (Nelson 1970). Search goods have attributes which can easily be obtained before the
purchase, for example before buying a chair, it is possible to try how comfortable it is.
Education is an example of experience good, what means that information about its quality
can be obtained only after the purchase. Typical student makes an educational decision on the
basis of limited or imperfect information (Jensen 2010).
Information about the quality of higher education is often scarce, unreliable and costly to
obtain, but is essential to make wise educational choices (McPherson, Winston 1995). Better
information is important not only to protect buyers from suboptimal decisions, but also to
provide an incentive for sellers to invest in quality and thereby better compete in the market
(Dill, Soo 2004). The experiment designed by Marietta Kiss and András István Kun (2014)
shows that the high quality colleges where better assessed by students, when they published
more specific information on their performance. Searching for information reduces risk of
wrong decision, and increases the probability of profitable and satisfactory investment.
Nowadays, in higher education policy there is a great emphasis on providing information
about academic quality to help students choose the most effective university (Dill 2013). In
2002 the Polish Accreditation Committee (PKA) was founded to enhance transparency of
education quality. In 2011 the Ministry of Science and Higher Education introduced the
obligation for universities to monitor graduates careers. There are more and more researches
of graduates’ situation on a labour market and many magazines publish rankings of
universities, which can help young people to make an educational decision are more and more
researches of graduates’ situation on a labour market and many magazines publish rankings of
universities, which can help young people to make an educational decision.
Access to information about cost and benefits is important for everyone facing the study
choice, but for pupils from low SES families this knowledge is crucial. First generation
students and their families often lack knowledge about higher education institutions and use
fewer sources of information than students from high SES backgrounds. In the United States
context, Cabrera and La Nasa (2000) shown that the only source of information about college
for low SES-students is high school counsellors. In contrast, students from high SES-families
who use a variety of sources including parents, students, catalogues, college representatives,
and private guidance counsellors. The lack of information affects college choices as low SESstudents tend to choose less selective colleges (Hoxby, Avery 2012). The study of low-income
families educational choices by Hastings and Weinstein (2008) show that receiving direct
information on school quality, the fraction choosing better performing schools has
significantly increased.
In Poland individuals coming from low SES backgrounds more often have to pay for studies
and more often attend low quality higher education institutions than their colleagues from
high SES families (Herbst 2012). Access to the knowledge of diploma value depends on
socio-economic status (Mikiewicz 2008), which may partly explain why low SES-graduates
are less satisfied with their educational choices (Herbst, Sobotka 2014). Individuals with less
educated parents, in particular, lack social capital and help from family to make wise
educational choices. Information they need may vary from information searched by high-SES
students. Therefore, it is important to assess the heterogeneity if information needs of future
students related to SES and different information needs of students.
Since the beginning of the 1990’s, the tertiary education market in Poland has significantly
changed – the number of students has increased fivefold and the enrolment rate for the age
cohort of 19-24 has increased from 9,8% in 1990/91 to 38,6% in 2013/14 (GUS 2014).
According to classification proposed by Martin Trow (1973), Polish model of higher
education has changed from elitist (up to 15% enrolled in higher education institutions) to
mass (between 15% and 40% enrolled in higher education institutions). The fast increasing
number of students was related to market transformation – in newly established market
economy, the demand for well-qualified workers was rising, and the high wage premium
caused the growth of educational aspirations (Liwiński, Sztanderska 2007). Regular surveys
by CBOS (2009) shown that in 1993 17% of Polish wanted for daughter a bachelor degree,
and 47% a master degree. In 2009 aspirations were much higher – only 9% wanted a bachelor
degree for daughter, 66% wanted a master degree and 11% a Ph.D. degree. The increase of
educational aspirations was the most marked among people with primary education level – in
1993 only 53% of them wanted a master degree for daughter and in 2009 75%.
Fulfillment of rising educational aspirations would not be possible without development of
private sector of higher education. In 1990’s many private universities were established and
public universities introduced part-time paid programmes. The “educational boom” had an
economic reason – higher education was investment with high return rate, a guarantee of wellpaid job and a way of social mobility. Study of wages in Poland conducted by Andrew Newell
and Mieczysław Socha (Newell, Socha 2007) shows that in 1994 the difference between
people with master’s degree and people with primary education level was about 41%, and
increased to 53% in 2002. Annual return rate to investment in higher education between 1988
and 2005 was about 6,5–9,5% and was one of the highest in Europe (Strawiński 2006) and in
countries at comparable level of development (Psacharopoulos, Patrinos 2004). The rapid
growth of people attending college, on one hand has led to the devaluation of higher
education diploma, but on the other hand, to its revaluation – diploma no longer is a guarantee
of being employed, but without diploma finding a job is very hard (Collins 1979, Czapiński,
Panek 2014). Investment in a bachelor's degree is no longer profitable, but masters’ degree
still gives financial advantages (Czapiński, Panek 2014). The value of PhD. degree is rising.
According to another CBOS survey (CBOS 2013) 78% of respondents think that higher
education is massive and available for everyone. Nearly half (47%) think that quality of
higher education is decreasing, and in opinion of 57% the diploma has little value on the
labour market. Despite that negative perception of higher education and education quality,
each year hundreds of thousands young people begin studies expecting that university
diploma will provide them a good job.
According to Lester Thurow's job-competition model (1975), even if benefits from education
are low, people will still have motivation to invest in additional education. The job
competition theory assumes that all workers have to be trained to the job, but they differ in the
cost of training. For employer level of education is a proxy of training costs – individuals with
higher level of education are more able, so the cost of their training is lower. The model
describes queue of workers competing for a job, where at the beginning are workers with the
lowest training costs, which will be hired first (Büchel 2003). If the number of jobs for highly
qualified employees is less than the number of people with high qualifications, some
employees, despite having similar qualifications as those at the head of the queue, will take
jobs for which they are overeducated. Workers at the end of the queue, with the highest cost
of training and lowest level of education, will remain out of employment.
The overeducation is a common problem in many European countries - around 30% of
individuals are employed below the level of qualification (Leuven, Oosterbeek, 2012). The
number of overeducated people in Poland is increasing. According to Kiersztyn (2011) around
one out of five Polish workers are in occupations below their skill level. Since 1988 the
mismatch increased almost threefold and overeducation is characterized by relatively high
persistence. The risk of being overeducated is several times higher among the youngest
people.
The second problem is employment status of graduates. Individuals with the highest education
levels are less prone to become unemployed. The unemployment rate is the lowest among
people with higher education (4,6%), and the highest (16,9 %) (GUS 2015) among those with
the lowest level of education. The problem is graduates unemployment, in first quarter of
2014 unemployment rate of college graduates was 23,7%. The unemployment rate among
graduates depends on major, college and the local labour market, e.g. in Pomorskie
Voievodeship in 2012, most unemployed graduates graduated in pedagogy, management, and
tourism and recreation (GUS 2013).
According to human capital theory, decisions about investment in education should be based
on cost-benefit analysis. Students as rational beings should choose only this studies which are
the most profitable. In the era of mass higher education the question is not whether to attend
to college, but which one to choose to get the high return rate (Hoxby 2007). The return rate
of investment in higher education depends on field of study, generally higher returns are in
engineering, medicine, business and sciences and lower in social sciences and humanities
(Gunderson, Orepoulos 2010).
Price of commodity or service is often a signal of its quality, and buyers often believe that
more expensive products have better quality. The tuition fees are very differentiated, and
question arises if the most expensive universities are worth their price. Brewer et al. (1999)
show that graduates from elite universities earn 40% more than their colleagues from less
prestigious institutions, but according to other studies (e.g. Dale, Krueger 2002), difference is
much smaller. Some researches argue (e.g. Manski 1993), that college choice is not about the
actual returns to education, but is more about the returns perceived by students and/or their
parents. e.g. Jensen (2010) shows that in the Dominican Republic the measured returns to
education are high, but the returns perceived by students are very low, partly because of
informational problems.
In Poland nearly 60% students pay for their studies and the value of tuition vary by university,
programme and region (Instytut Sokratesa 2011). The most expensive are medical studies, art
studies and architecture, ten times cheaper is to study theology or musicology. The most
expensive studies are in Masovian Voivodeship and the cheapest in Podkarpackie. The
profitability of investment in education differs by major, e.g. the rate of return on investment
in information technology studies in 2013 was 90%, much less than investments in humanities
and social sciences (32%), and in the case of agricultural studies was (Czapiński, Panek
2014). The return rate to investment in education depends also on region and gender. The
highest relative benefits receive women from Lublin and Białystok, and the lowest men from
Szczecin (Herbst 2012).
Research conducted by Baranowska-Rataj (Baranowska 2011, Baranowska-Rataj) shows that
graduates from private universities found first job at the same time as graduates from public
universities, but they had smaller chances of finding high quality job. The research of the role
of diploma brand conducted by Dominik Antonowicz, Magdalena Krawczyk-Radwan and
Dominika Walczak (2011) showed that for employers the brand of university is the highest on
the preselection phase and especially for position where employers plan to train the worker
and do not have specific expectations.
Information about real costs and benefits of diploma can attract individuals to invest in
diploma or scare them off. Since school year 2010/11 for the first time from the beginning of
1990s, the enrolment rate in higher education is decreasing. Decline on national level is not
high (2,2 pp. net enrolment rate, 4,6 pp. gross enrolment rate, GUS 2014), but the regional
differences are much greater. Is it possible that this first decline after twenty years of
educational boom is due to the better information about growing number of young
unemployed people with college diploma?
The problem of imperfect or asymmetric information in higher education market in Poland is
noticed (Liwiński, Sztanderska 2007, Sztanderska 2014, Rószkiewicz, Saczuk 2014), but has
not been analysed on empirical data. The conducted research will substantially enrich the
academic knowledge about the phenomenon of imperfect information in educational market.
3. Methodology
The main analyses will be conducted on existing data on higher education. All publications
of Central Statistical Office and Regional Statistical Offices, the Ministry of Labour and
Social Politics, as well as other studies and research results (ex. Study on Determinants of
Educational Choices by Educational Research Institute, Study of Human Capital in Poland by
Polish Agency for Enterprise Development) concerning occupational situation of college
graduates will be collected to calculate changes in access to information. The most important
measures of graduates benefits from investment in education will be i.a.: employment rate on
national and regional level, employment rate by major, level of income on national and
regional level, the earnings by major and college, return rate to higher education, et.al. The
additional measure of access to information will be the outreach of particular research results,
calculated by the number of articles in the mostly read daily and weekly magazines which has
been informing about college graduates situation in the labour market. The quantified
information will be perched in database to calculate basic statistics, which will show how
access to information about the benefits of higher education has changed over the last 15
years and what is differentiation between regions and majors in access to the information. The
outcomes will also include changes in profitability of higher education in analysed period.
The analysis of educational choices in the context of existing data on graduates
unemployment will be carried out in two ways. First, cross-sectional analysis will be
performed on available databases that allow the analyses of determinants of educational
decisions on individual level, including the analyses selection mechanisms (e.g. Heckman
model), and then time-series analysis, presenting changes in structure of educational choices
at national and regional level. The quantified information about the benefits of education,
including the return rate to investment in higher education, will be combined with data from
the Central Statistical Office on actual major choices in the next years. The purpose of time
series analysis is to check whether there is some correlation between the analysed data and
use of published information to make rational education choices.
The analysis of information used by colleges to monitor graduates’ careers will be carried out
on reports from monitoring of college graduates careers from the years 2011-2014. During
that period colleges were obliged by the Ministry of Science and Social Politics to monitor the
situation of the graduates in the labour market. This analysis will show how many colleges
published such materials on their websites, what kind of measures are the most commonly
used by colleges to monitor, and which information is lacking.
To analyze students information needs the qualitative and quantitative methods will be
used. First, it is planned to conduct some individual interviews with the students in their final
year of secondary schools and first-year students from different socio-economic background.
Interviews will let to identify the most important (according to students) indicators of college
quality. Students will be asked to indicate source and types of information which helped them
pick the college and information which they lacked. The gathered material will be analysed in
the context of the influence of social background on information needs.
The quantitative part will be based on trend analysis of the most often searched terms related
to higher education by using Google Trends Tool. Google Trends show how often a particular
search-term is entered relative to the total search-volume. The outcomes are presented on the
graph where one axe shows slot of time defined by user and other axe shows how often a term
was searched for relative to the total number of searches. The search-terms can by analysed
geographically by countries, regions and cities. This innovative tool is used worldwide to
measure variety of human behaviors and social phenomenon.
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