DRAFT FOR DISCUSSION – DO NOT CITE

The Equity of the Higher Education System: Case Study-Poland

Mikolaj Herbst

Jakub Rok

October 2010

Table of Contents Table of Contents .................................................................................................................................... 2

Introduction ............................................................................................................................................. 3

Institutional arrangements and higher education funding in Poland ....................................................... 4

Tertiary education: basic facts ............................................................................................................ 4

Tertiary education: financial resources ............................................................................................... 6

Student support system and tuition cost .............................................................................................. 8

Tertiary education participation compared to OECD standards ........................................................... 11

Inequalities at earlier stages of education ............................................................................................. 14

Accessibility to higher education in Poland .......................................................................................... 21

Earlier studies: main findings ........................................................................................................... 21

Determinants of tertiary education access during the transformation period[ .................................. 22

Full-time versus part-time students. Who they are. .......................................................................... 28

For whom have the private tertiary institutions emerged? ................................................................ 31

Who pays for attending tertiary program? ........................................................................................ 33

Logistic model of tertiary school accessibility ................................................................................. 34

Equity of access to higher education in Poland versus other European countries ............................ 38

A demographic factor ....................................................................................................................... 40

Quality concerns – hidden inequity in tertiary education? .................................................................... 41

Quality of tertiary schooled: related to available resources .............................................................. 43

The quality of tertiary education and graduates‟ labor market performance .................................... 47

Conclusions ........................................................................................................................................... 56

Policy recommendations ....................................................................................................................... 59

Appendix. Results of the logistic regression of tertiary school accessibility ........................................ 63

List of Tables ........................................................................................................................................ 64

List of Figures ....................................................................................................................................... 65

References ............................................................................................................................................. 66

3

Introduction

1. The two decades of transformation after 1989 were a time of profound

changes in both Polish economy and society. And the development of the

higher education sector was spectacular. In response to the growing

demand for general education (partly resulting from the collapse of old

industries), a formerly elitist system serving less than 10% of young

people in Poland, evolved into a system with mass open enrollment. The

number of students increased fivefold, and the number of higher

education institutions increased fourfold. Growth was achieved both by

expanding the capacity of already existing, publicly owned tertiary

(postsecondary) schools, and by allowing private capital to invest in

education. Today, one-third of tertiary students attend private schools.

There is no doubt that these developments substantially improved the

accessibility of higher education for Poland‟s entire social strata. Still, the

equity of the system is frequently questioned in the public debate, as the

different forms of tertiary education are subject to differing regulations;

they vary with respect to the financial burden imposed directly on

students and also, presumably, with the quality of the programs. The goal

of this research is to assess, taking into consideration all these effects,

how the transformation influenced the equity of the higher education

system.

4

Institutional arrangements and higher education funding in Poland

Tertiary education: basic facts

1. The economic transformation launched in 1989 resulted in a

significant increase in the demand for general education. The

structure of secondary school profiles in the early 1990s corresponded to

the needs of a centrally planned economy, with a high proportion of

employment in the industrial and agricultural sectors. During the school

year 1990/1991, 76.5% of secondary students attended various types of

vocational schools, and only 23.5% chose the general education path. Ten

years later, the proportions had already changed, with 62.3% of

secondary students enrolled at vocational programs – the most notable

drop (by 20 percentage points) was in basic vocational schools.

According to the most recent data for 2008/2009, the general education

program was attended by 43.6% of secondary students and vocational

schools were attended by 56.4%. The number of tertiary students has

risen from 404 thousands in the academic year 1990/1991 to 1.928

thousands in 2008/2009 – a nearly fivefold increase. Simultaneously, the

number of tertiary schools has increased from 112 to 456 (Główny Urząd

Statystyczny 2009).

2. The rapid growth of higher education enrollment would not have

been possible without allowing private capital to invest in education.

Until 1990 all existing higher education institutions were state owned.

Since the academic year 1990/1991 and the introduction of the new Law

on Higher Education (1990), private tertiary schools1 have been

established and public schools can offer paid part-time programs. In the

academic year 2008/2009, in addition to 131 operating public tertiary

school, there were 325 nonpublic institutions, attended by one-third of

Polish tertiary students (ISCED 5A/6 programs), compared to 13.7% on

average in the OECD. Part-time study, chosen by 46.7% of students, was

more prevalent than on average in the OECD (20.1%). Foreign

enrollments represented less than 1% of tertiary students (OECD 2009).

3. Nonpublic schools tend to be smaller, are attended mainly by part-

time students and are predominantly vocational. The nonpublic sector

of tertiary education in Poland is dispersed, being present in 172 out of

1 Later, also referred to as „nonpublic institutions/schools/sector.‟ These schools fall into the OECD category of

independent private institutions.

5

379 counties (powiaty). Fifty-four percent of nonpublic schools offer

fewer than 4 faculties and the number of students per nonpublic tertiary

educational institution (TEI) averages 2,000; this is fivefold less than in

the public sector (Ernst & Young and Instytut Badań nad Gospodarką

Rynkową 2009). The portion of part-time students varies from 81.7% in

nonpublic schools to 36.3% in the public sector (see Table 1) and, since

2006, the majority of part-time students are at nonpublic TEIs. In both

cases ”social sciences, business and law” constitutes the most popular

field of study, attended by 56% of students in the nonpublic sector and

34% in public schools. Few nonpublic institutions can award doctoral

degrees (only 5 schools for the academic year 2003/2004), and most

(75%) offer only bachelor‟s degree programs (OECD 2007).

Students

(total of)

Females

(%)

Full-time

students

Part-time

students

Institutions

(total of)

Students

per TEI

Public TEIs 1,268 55.8 808 461 131 9.7

Nonpublic TEIs 659 59.2 121 539 325 2.0

Total 1,928 57.0 928 1 000 456 4.2

Table 1. Enrollment in Polish tertiary education, academic year 2008/09 Source: (Ernst&Young and Instytut Badań nad Gospodarką Rynkową 2009); (Główny Urząd Statystyczny

2009).

4. Unlike public schools, which receive substantial subsidies from the

state (mainly for teaching activities) the nonpublic sector relies

financially on tuition fees paid by students, constituting 83,7% of its

total revenues, compared to 14,4% of public tertiary schools‟ revenues.

Tuition fees are only required from students in nonpublic schools

(regardless of the mode of studying) and from those enrolled in part-time

programs in public institutions. Teaching activity is the main source of

income for the higher education sector, bringing 84.4% and 98.3% of

total revenues for public and nonpublic institutions respectively, while

research activities account for 14.8% and 0.4% respectively Public

sources in the Polish tertiary education system account for 62% of funds

for teaching activities and 82% of funds for research activities (Główny

Urząd Statystyczny 2009).

5. A significant part of resources, both human and financial, in Polish

tertiary education is concentrated in a small number of TEIs. A

group of 25 (out of 456) Polish TEIs collects 84% of the sector‟s total

income from research activities and 60% of the revenues from teaching

6

activities (Ernst & Young and Instytut Badań nad Gospodarką Rynkową

2009). Eighteen universities and 27 technical universities have over 50%

of all academic teachers and 42% of the students. Only two Polish TEIs

are included in the Academic Ranking of World Universities (“Shanghai”

ranking): Jagiellonian University and Warsaw University. The latter is the

biggest TEI in Poland, with 56,000 students enrolled.

6. The 1990 act emphasized the expansion of overall enrollment

considerably more than it directly addressed equity of access. As

summarized in the OECD review of tertiary education in Poland, the

strategy of achieving equity through the expansion of access relies on two

main approaches: (1) financial assistance for low-income students

through a range of grant schemes and other programs; (2) expansion of the supply of

tertiary programs, with the creation of institutions in smaller cities. According to OECD

experts, equity is not among the priorities of Polish tertiary education policy – e.g., little data

on student's background arecollected and a relatively small share of public funds is allocated

to needs-based schemes. Also, the equity of outcomes receives minimal attention – little

emphasis is placed on students‟ progression or on assisting disadvantaged (socially or

academically) students (OECD 2007)

Tertiary education: financial resources

7. Public expenditures on higher education in Poland, increasing in

recent years, are still very low per student. The share of public funds

assigned to R&D is more than two times less than the OECD average.

The public spending on tertiary schooling in 2008 equaled PLN (a Polish

currency unit) 11.1 billion, of which 8.8 billion was spent as direct

subsidies for teaching activities (99.1% going to public tertiary

institutions). Other main components of public allocation included funds

for financing statutory activities and the subsidy for students‟ grant

schemes, the latter also being available for nonpublic schools. In 2006,

spending per student (considering public institutions only) equaled 5,224

USD annually, compared to 12,336 on average in the OECD. The share

of this value allocated to R&D amounted to 14% in Poland compared to

the OECD average of 31% (see Figure 1)(OECD 2009). Gross

expenditures in Poland were growing rapidly (by 69%) over the period of

2000–2008, but the expanding number of students resulted in growth per

student at 39%. In 2008, the total per-student cost in the whole sector

equaled PLN 8564 (4435 in purchasing power adjusted USD), with unit

cost in the public sector being three times higher than in nonpublic

7

schools. Per-student public expenditures on teaching activities in 2008

varied, from PLN 119 for nonpublic TEIs, to up to PLN 4567 (2365 in

USD) in public institutions.

Figure 1. Annual expenditure per tertiary student by type of service, 2006 Source: (OECD 2009)

Note: Data for Denmark, Japan and Iceland without service specification; no data for Greece; Canada

and Poland – public institutions only; Turkey – no data on R&D.

8. Poland’s total expenditure on tertiary education as a ratio to GDP is

about the OECD average, though the share of funds from private

sources is slightly above the OECD average. In 2006, total spending on

tertiary education in Poland equaled to 1.3% of its GDP, close to OECD

average of 1.5%. Out of this number the public subsidies on t ertiary

education accounted for 0.9% of GDP, while the private expenditures

(tuition fees) for the remaining 0.4%. Therefore 30% of the total spending

derives from private sources (constituted only by household

expenditures), which is more that the OECD average of 27% (see Figure

2). According to estimations of Central Statistical Office (GUS 2009),

level of public spending in relation to GDP was sustained in 2008 (0.88%

of GDP) and total expenditure amounted to 1.3% of GDP.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

24000

26000 Annual expenditure per tertiary student, 2006In equivalent USD converted using PPPs for GDP, by type of service, based on full-time

equivalents

R & D Ancillary services Educational core services

8

Figure 2. Total public and private expenditure on tertiary education, 2006. Data for Poland (2008) based on (Główny Urząd Statystyczny 2009)

Source (OECD 2008)

Student support system and tuition cost

9. The student support system in Poland relies on several types of

benefits and the student loan scheme. All support comes from the state

budget and it is transferred to tertiary schools in a form of special

subvention. Students from low income households may apply for social

benefit, the meal benefit or the housing benefit. Since 2001 students are

eligible for state support irrespective of sector (public or private), mode

of study and degree level. One exception is the housing benefit, available

for full-time students only. Since 2002 a special program for disabled

students was introduced, providing various forms of support. In academic

year 2008/2009 disabled students accounted for 1.31% of student

population, and 96% of them were receiving some form of nonrepayable

financial assistance (GUS 2009). In total, about 7.5% of total public

spending on tertiary education was devoted to income based assistance

for students, and another 0.5% was for disabled students (Główny Urząd

Statystyczny 2009). In sum, all forms of material assistance for students

(including merit based grants, but excluding loans) accounted for 11.5%

of total expenditures on tertiary education. This proportion is similar to

OECD‟s 2006 average of 10,2% from 2006 (OECD 2009). Public funds

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Un

ited

Sta

tes

Can

ada

(20

05

)

Ko

rea

Fin

lan

d

Den

mar

k

Au

stra

lia

Swed

en

New

Zea

lan

d

OE

CD

av

erag

e

Jap

an

Net

her

lan

ds

Po

rtu

gal

Po

lan

d (

20

08

)

Po

lan

d (

20

06

)

Fra

nce

Au

stri

a

Bel

giu

m

Un

ited

Kin

gdo

m

Cze

ch R

epu

bli

c

Irel

and

Mex

ico

Icel

and

Hu

nga

ry

Spai

n

Ger

man

y

Slo

vak

Rep

ub

lic

Ital

y

Private

Public

9

assigned to income based assistance amounted to PLN 430 per student in

2008, and the average assistance granted was PLN 2432 (yearly). In the

academic year 2008/09, around one-fourth of the student population was

receiving some form of student support (see Table 2).

TEIs Mode of

study

Student benefits from grants

Social grants Merit grants Disability grants

Number

of

(in th.)

% of Average

amount

Number

of

(in th.)

% of Average

amount

Number

of

(in th.)

Average

amount

Public Full-time 134.5 16.9% 242 PLN

127.5 16.0% 343 PLN

15.4

203 PLN

Part-time 32.5 7.1% 46.0 10.1%

Nonpublic Full-time 19.0 15.8% 246 PLN

17.0 14.1% 205 PLN 8.9 280 PLN

Part-time 51.0 9.5% 56.5 10.5%

Total 237,0 12.4% - 247.0 12.9% - 24.3 -

Table 2. Students benefitting from state support system, academic year

2008/2009 Source: (Główny Urząd Statystyczny 2009), (Ernst&Young and Instytut Badań nad Gospodarką Rynkową

2009).

Note: Individuals are allowed to receive more than one grant benefit if eligible.

10. Since the majority of students in Poland pay tuition fees, while the

remaining students’ studies are fully subsidized by the public, the

burden of private contributions is borne by part of the student

Mainstreaming disabled students in Polish tertiary education system

Since 2002, a special program for disabled students has provided various forms of support:

grants to assist with the study costs (tuition fees, accommodation expenses, travelling

expenses), grants to facilitate enrollment in foreign institutions, and scholarships for

outstanding educational achievements. Students can benefit from several programs

simultaneosly, including social and merit grants (OECD 2007).

In 2008, disabled students accounted for 1.31% of the tertiary student population (compared

to 0.26% in 2002), and 96% of them were receiving some form of nonrepayable financial

assistance (GUS 2009). Increased enrollment might be attributed to efforts at both the

national level (grants for disabled students) and the institutional level (buildings adapted to

accommodate disabled students).

10

population and not shared by all. The number of students paying

tuition fees (i.e., part-time students from public TEIs and all the students

from nonpublic TEIs) reached 1.1 million in the academic year

2008/2009, accounting for 58% of the total student population in Poland

(Ernst&Young and Instytut Badań nad Gospodarką Rynkową 2009). The

tuition fees are low. The average annual fee paid at ISCED 5A nonpublic

institutions in academic year 2004/2005 equaled 2.710 USD (PPP) – a

similar amount to the Czech Republic. According to other sources of

data, yearly tuition fees in the academic year 2003/2004 were ranging

between PLN 1600 and 80002 across different institutions (OECD 2007).

11. Besides nonrepayable forms of support, students in Poland are

eligible for a loan under the student loan scheme (launched

1998/1999). Up until the 2008/2009 school year, nearly 318,000

students have been granted a loan, but the popularity of this form of

support has been significantly decreasing over the last ten years. In the

1998/1999 school year, 100,000 students took a loan, in the following

period this number stabilized at around 22–28 thousands yearly, but

recently fell to 13 thousands(Konferencja Rektorów Akademickich Szkół

Polskich 2009). According to OECD authors (OECD 2007), up until

2004/2005 only 22% of grants were taken by students from the nonpublic

sector. The main reason for the low and decreasing usage of student loans

was that the banks offering them imposed their own eligibility criteria

based on standard risk assessment procedure, preventing less affluent

students, who really needed assistance, from using it (see the text box

below for details). Additionally, according to OECD experts the amounts

of loans were not sufficient to cover real costs of living and tuition fees,

depressing the demand for this form of assistance (OECD 2007).

2 After converting these amounts using PPP (2004), fees range between USD 450 and 2200

11

Tertiary education participation compared to OECD standards

12. Poland has quickly closed its gap with OECD countries with respect

to higher education attainment and surpasses the OECD average for

tertiary enrollment among individuals less than 29 years old. The

attainment rate among individuals ages 25–64 is still lower than OECD

average (19% in 2006 as compared to 27%), but in the cohort of persons

ages 25–34 this difference is notably smaller (30% compared to 34%) and

Student loan scheme in Poland and changes for 2010/2011

In 1998, a student loan scheme was introduced in Poland, with eligibility limited to undergraduate

and graduate students with Polish citizenship (or EU nationals resident in Poland), who enrolled in a

TEI when under 26 years of age. Due to budget constraints, a maximum household income per

capita was also specified as an eligibility criterion. In 1999/2000 this threshold was set at PLN 550,

but was then increased almost each year, reaching PLN 2,500 in 2008/2009.

A loan can be granted for up to 6 years and is transferred to the beneficiary in 10 monthly

installments of PLN 600. The interest rate is subsidized by the state, and it is favorable compared to

commercial loans. It equals 50% of the rediscount rate on the National Bank of Poland. The Ministry

of Science and Higher Education recently prepared a project according to which the interest rate will

vary based on the student‟s income. For those living in a household with an income below PLN 602

per head, the interest rate will remain at 50% of the rediscount rate. More affluent students will face

an interest rate of 130% of the rediscount rate.

To receive a loan, students need to provide a guarantor and undergo a standard risk assessment

procedure, which in the past excluded the less-well-off individuals. The changes planned for

2010/2011 address this issue by offering a 100% state guarantee for loans taken by the low-income

students (currently they may apply for 70%).

The loan repayment begins two years after graduation. It may be partly or fully remitted due to

exceptionally difficult situations for the graduate. The debt may be also reduced by up to 20%

(which will increase to 30% as of 2010/2011) as a reward for high educational achievements

(graduation within the top 5% of the students in a given school).

12

almost equals the EU19 result of 31%. The decreasing gap is caused by a

rapid increase in individuals with tertiary education in Poland, at a yearly

rate exceeding 7%, compared to the OECD average of 4.5% a year (see

Figure 3). Among the younger cohorts, participation in tertiary education

in Poland is high. The enrollment ratio for individuals ages 20–29 equals

31% (compared with 25% on average in the OECD) and has been stable

since 2005. When it comes to ages 15–19, Poland‟s enrollment rate is the

second highest among all OECD countries. Though, according to 2009

demographic predictions, Poland should expect a downturn by 41% for

persons ages 19–24 in 2025, as compared to 2008 figures (Główny Urząd

Statystyczny 2009).

Figure 3. Annual average growth of tertiary education for ages 25–64 (1998–

2006)

Source: (OECD 2009)

Among the younger cohorts, participation in tertiary education in Poland

is high. The enrollment ratio for individuals ages 20–29 is 31%

(compared with 25% on average in the OECD) and has been stable since

2005. When it comes to persons ages 15–19, Poland‟s enrollment rate is

the second highest among all OECD countries.

13. More than one-third (34.2% in 2008) of Polish tertiary students

attend independent education institutions as compared to 13.7% (in

2007) on average in OECD. Foreign enrollments represent less than 1%

0123456789

[%]Annual average growth of tertiary education for ages 25-64 (1998-2006)

13

of tertiary students. Part-time study is more prevalent in Poland than in

OECD on average (51.9% compared to 20.1%). The education

expectancy at the tertiary level is 3.4 years, while OECD averages 3.1

years. Fifteen-year-olds are expected to spend 8 years more in education,

of which 1.4 year employed (OECD: 6.7 and 2); 28% of persons ages 20–

24 in education are employed (OECD: 34%).

14. The entry rate to academically oriented (ISCED 5A) tertiary

education in Poland for upper secondary school graduates in 2007

exceeded the OECD mean value by 22 percentage points (78% to

56%, giving Poland second place among OECD countries). The high

entry rate indicates the good accessibility of tertiary education and

corresponds with the perceived value of attending these programs. The

graduation rate from secondary programs aimed at preparing for tertiary

education (ISCED 3A), equaling 77%, is also well above the OECD

average of 61% (see Figure 4). The age of new entrants is relatively low

in Poland, 80% of them are less than 23 years old (no comparison to

OECD average). The graduation rate for level 5A is notably high (49%

compared to 39%), especially if taking into account the length of tertiary

education programs.

0

20

40

60

80

100

Access to tertiary type-A education for upper-secondary graduates, 2007

graduation rates from upper-sec. programs (ISCED 3A) designed to prepare for entering ISCED 5A

entry rates into ISCED 5A for ISCED 3A graduates

14

Figure 4. Secondary graduation (ISCED 3A) rates and tertiary entry rates

(ISCED 5A), 2007 Source: (OECD 2009)

Note: No data for Canada, France, Iceland, Luxembourg, New Zealand, United States and United

Kingdom.

Inequalities at earlier stages of education

15. The issue of access to higher education should not be approached

without examining equity at the earlier stages of schooling, with

particular focus on tracking within the upper-secondary education

level, which strongly influences one’s chances to enter the tertiary

education level. The tertiary level, being the final stage of school

education, accumulates the effects that have taken place at the lower

levels. The recent World Bank report on the Polish education system

(Rodriguez and Herbst 2010) suggests that the most unequal access to

education is observed at preschool level. While there are no signs of

inequity in access to primary or lower-secondary school (the enrollment

rates at these tiers are close to 100%), the OECD PISA Program,

measuring the performance of ninth-grade students (leaving lower-

secondary school), allows us to evaluate some equity effects on schooling

at this stage in the cross-country comparative framework. The important

stage is when students choose their secondary schools, since at this point

they are divided into general education track (likely to continue at the

tertiary level), secondary vocational (retaining the possibility of entering

higher education) and basic vocational (not providing full secondary

education). This tracking determines, to a large extent, the future careers

of students, and is of crucial importance while evaluating the equity

effects of the whole education system.

16. Participation in preschool education is generally low (but increasing)

in Poland, and its availability is much worse in rural areas than in

the cities. In 2006 the enrollment rate among children ages 3–5 years was

only 41%, which was lower than in any other EU country. Rural areas are

particularly affected by low access, with only 19% of children enrolled,

as compared to 62% in the cities. This difference in educational

opportunity, so early in life, creates far different outlooks for the future of

15

children in rural areas. However, the access to preschool education is

improving fast, after allowing for establishing the “alternative forms.”

The participation rate among children ages 3–5 years in 2009 grew to

59.7%.

17. Total variation in student achievement at the lower-secondary level is

below the average OECD level, and the differences between schools

are smaller than in most other countries. The total variation in the

2006 PISA science test in Poland was equal to 90.8% of the OECD

average. The ratio of total variation attributable to differences between

schools (between-school variance) in Poland to the average variance in

student performance in OECD countries is notably small – 12.2%,

compared to 33% on average in the OECD3 (only Scandinavian countries

report lower results; see Table 7). It indicates that, in the case of Poland,

performance is not closely related to the schools in which students are

enrolled. Whereas, within-school variance in Poland amounts to 78.6% of

the average variance between OECD countries in student performance on

the PISA science scale, outweighing OECD average of 68.1%.

3 Poland experienced significant decrease of this parameter during the 2000–2006 period (from 50,7% to 12,2%)

associated with the 1999 reform of the educational system. This dramatic change is mainly attributable to the

fact that, since the reform, 15-year-old students are not yet divided into different school tracks.

0

10

20

30

40

50

60

70

[%]

Variance in student performance between schools on the science scale, 2006

Expressed as a % of the average variance in student performance in OECD countries

total between-school variance

between-school variance explained by the ESCS of students and schools

16

Figure 5. Variance in student performance between schools, on the science

scale, 2006 Source: (OECD 2007)

18. The impact of students’ socioeconomic status on their achievements

in the PISA science test is close to the OECD average, with the

exception of parental education, exerting stronger effect in Poland

than in other countries. Home background remains one of the most

powerful factors influencing student‟s performance, explaining an

average of 14.4% of the student performance variation in science in the

OECD area, and 14.5% in the case of Poland (latter value not statistically

significant). The ESCS (PISA index of economic, social and cultural

status) at both the student and school levels plays a weaker role in

explaining between-school variance in Poland than on average in OECD.

Conversely, students‟ socioeconomic status has a stronger effect on

within-school variance in Poland than in OECD countries. In both cases,

results are similar (i.e., not exceeding one percentage point), if reading or

mathematics is examined. Polish students with a more advantageous

socio-economic status (top quarter) score on average 86 points more on

science scale than their peers from bottom quarter (OECD: 92 points).

The impact of different components of the ESCS on students‟ mean score

in the PISA 2006 science test is shown in Table 3.

By the ESCS Parents’ education level Parents’ occupational

status

Level of cultural

possessions in home

Home educational

resources

Bottom

quarter

Top

quarter

Diff. ISCED 2

or below

ISCED

5 or 6

Diff. Blue-

collar,

low-

skilled

White-

collar,

high-

skilled

Diff. Bottom

quarter

Top

quarter

Diff. Bottom

quarter

Top

quarter

Diff.

Poland 460 546 86 432 (4.6) 553

(18.6)

121 468

(5.3)

528

(44.3)

60 467 522 55 461 514 53

OECD

average

456 547 91 446 (15) 525

(46.6)

79 454

(7.7)

527

(54.3)

73 470 534 64 468 518 50

Table 3. Student performance on the science scale by elements of the PISA index of economic, social and cultural status

(ESCS), 2006 Values in brackets express % of students in the particular group.

Source: (OECD 2007)

Figure 6. Participation in general secondary education of persons ages 16–18 years by quintiles of household revenues per capita (1994–2008)

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

1

2

3

4

5

18

Source: Author‟s calculations based on household survey data

19. The selection of different upper-secondary tracks is crucial in

determining future educational attainment. The probability of being

in the general track (most likely to continue at tertiary level) clearly

depends on the material status of the student’s family. When

graduating from lower-secondary school, a student chooses between three

upper-secondary education profiles. General and vocational-secondary

program graduates receive a full secondary education, which (after

passing the final examinations) allows admission to tertiary schools.

Continuing education at the tertiary level is more likely by 25 percentage

points for students following the general rather than the vocational path,

but the latter receive training in a particular profession during their

secondary program (Edukacja dla pracy..2007). In turn, basic vocational

education prepares students for direct entry to the labor market. Thus,

from equity perspective, it is important to understand the relationship

between socioeconomic status and upper-secondary profile choice made

by students. As shown in Figure 6, in the mid-1990s only 12% of students

coming from the least affluent households (1st quintile of the per capita

income) were choosing the general path at the upper-secondary level. In

turn, a similar decision was made by 46% of students from a high-income

environment (5th quintile). During the late 1990s and the 2000s the

probability of obtaining general secondary education was increasing in all

income groups, although the gap between these groups did not close, as

enrollment was increasing at a steeper rate for more affluent students. The

difference in participation in general secondary education between the

highest and the lowest income quintiles began to drop in 2003, as the

demand of the most affluent households was satisfied. Eventually, in the

late 2000s the enrollment rate within income quintiles 3 to 5 began to fall,

while it was still increasing for the 1st and 2

nd quintiles.

20. The correlation between family educational background and the

probability of choosing the general secondary path is much stronger

than in the case of household income. It was so in the early 1990s,

when two-thirds of students with a father holding a university degree

followed the general path, compared to ten percent of those with only a

primary-level family educational background. Again, during the

20

transformation period, the demand for general education increased among

all groups, by 2004 reaching the enrollment rate of 70% and 50%

respectively for students with fathers at tertiary- and secondary-education

levels (see Figure 7). Between 2005 and 2008 the rates for both these

groups had not changed, indicating that the transformation-induced

adjustment in education demand has ended and the new equilibrium level

has been achieved. Simultaneously, the general-secondary enrollment rate

among students with weaker educational backgrounds has been

increasing for the whole 1994–2008 period, eventually reaching 26% and

38% for students whose fathers have primary and basic vocational

educations, respectively.

Figure 7. Participation in general secondary education for persons ages 16–18

years, by father‟s education level (1994–2008)

Source: Author‟s calculations based on household survey data

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

19

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00

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08

tertiary

secondary

basic vocational

primary

21

Accessibility to higher education in Poland

Earlier studies: main findings

21. In the 1990s, a growing impact of social background on the access to

tertiary education was observed. According to the survey undertaken in

1999 (Domański 2000) , students whose father worked as a manager or

brainworker had a four times higher chance of entering tertiary schooling

than did those whose father had an unskilled blue-collar background.

Students from farm and skilled blue-collar occupational backgrounds

were also significantly underrepresented. Domański concludes that access

to the public TEIs in Poland is “hereditary.”

22. Świerzbowska-Kowalik and Gulczńska (Świerzbowska-Kowalik and

Gulczyńska 2000) found that students whose parents have higher

education have better access to the free-of-charge graduate studies in

traditional academic centers than do their peers whose parents didn’t

attain tertiary level. The conclusion was based on a comprehensive

survey of tertiary students, undertaken at the end of 1999, that measured

the impact of socioeconomic background on access to higher education.

Additionally, out of the students with primary education background

enrolled in tuition free programs only 1 in 14 declared he or she could

afford paying a tuition fee, while for those with a higher education

background it was 1 in 5. Surveyed students, evoking their pre-admission

predictions of income sources during studies, had counted on income

from paid employment rather than on state support. Also, 37% of students

claimed they preferred to cover the cost of studying with income from

employment than with the student loan. One-fourth of surveyed students

claimed that the real cost of studying exceeds the amount they had

expected, with 40% of them pointing, in particular, to the costs of course

books and teaching materials. More than 50% of students were working

at the time the survey was taken, because they found it necessary given

their financial situation. Some 14% of students were able to cover all

their expenses with income from employment, while 18% did so with

some family support. Half the students still lived with their parents,

20.9% were renting a room or flat, around 17% had their own apartments,

and less than 10% lived in a student hall.

22

23. Białecki (Białecki 2003), drawing on the data from the representative

group of individuals born 1977–1978, estimates that students whose

fathers completed tertiary education are 4.5 times4 more likely to

enter tertiary education than students whose fathers completed

primary or prevocational education. This ratio varies at 4.2 for females

and 4.9 for males. Interestingly, the mothers‟ educational attainment level

exerts a stronger and growing influence on the likelihood of tertiary

enrollment. In 2005, the probability of entering tertiary education for

upper-secondary graduates with mothers holding a university degree was

higher by 22 percentage points, as compared to those with a primary

education background. When fathers are taken into account, the

probability gap was only 15.2 percentage points (Sztanderska and

Liwiński 2007).

24. Entering upper-secondary and tertiary education are both influenced

by parents’ educational attainment level, but the selection at the

earlier stage determines the educational career to a larger extent.

Tracking into different paths of secondary education strongly depends on

the students‟ educational background, which strongly influences the

chances of admission to tertiary education. Sztanderska and Liwiński

claim that, since 85% of graduates from the general path of upper-

secondary education find their way to tertiary education, the first filter

seems to be crucial for shaping the future educational path (Sztanderska

and Liwiński 2007) .

Determinants of tertiary education access during the transformation

period5[

25. The participation in tertiary education of persons ages 19–26 years

grew dynamically during the 1990s and 2000s, with significantly

higher increases among females than males. Given the unusual rate of

increase in the number of students and tertiary education institutions

reflected in Polish statistical data (see the “Introduction” section of this

4 Eighty-two percent of individuals whose father attained tertiary education, and only eighteen percent of

individuals whose father attained a primary or prevocational education, access TEIs. 5 The results presented in this chapter are based on the analysis performed on Household Budget Survey data

(Badanie Budżetów Gospodarstw Domowych GUS) covering 1994–2008. Some indicators of accessibility

provided in this part may differ from the official statistics discussed in the introductory chapter, since the

survey-based analysis focuses on individuals ages 19–26 years.

23

report), one cannot expect anything else than the strong improvement in

accessibility of higher education across all social and economic groups.

Indeed, the enrollment rate at the tertiary level within the 19–26-years-old

age group increased from 8% in 1994 to 34.5% in 2008. As shown in

Figure 8, the changes particularly benefitted women. In the early 1990s,

participation in higher education was similar for both sexes, although

earlier it had been higher for males than for females. The economic

transformation and developments in the education sector have reversed

this: from the mid-1990s on, young females have been more likely to

study at the tertiary level than their male peers. Since then, the gap

between sexes has been steadily increasing, reaching 8.7 percentage

points in 2008 (30.1% for males and 38.8 for females).

Figure 8. Persons ages 19–26 years attending any tertiary program (1994–2008)

Source: Author‟s calculations based on household survey data

26. The transformation generated the demand for education among

young people in Poland, but the increase in participation rate within

other age groups was much smaller. For instance, the portion of tertiary

students among the 27–35-years-old age cohort, which was below 0,5%

in 1994, reached 4% in 2003 and remains at this level. In turn, the rate

for the 36–50-years-old age cohort has increased from 0% to 1.2% during

1994–2008. Therefore, the easier access to higher education has been a

moderately used opportunity to catch up by those who, for some reason,

did not enter the tertiary level directly after graduating from secondary

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

Males

Females

24

school. Those members of older age cohorts who decide to study at the

tertiary level do it predominantly part-time and in nonpublic institutions.

27. The enrollment rate has been increasing within all income groups..

However, although the higher education accessibility for the less

affluent groups has improved in absolute terms, the relative

accessibility (compared to that experienced by affluent individuals)

has been decreasing for most of the period considered (see Figure 9).

Evaluating whether the changes led to more or less equitable situations in

particular years depends on the measures applied.

Figure 9. Participation in tertiary education for persons ages 19–26 years, by

quintiles of household revenues per capita (1994–2008)

Source: Author‟s calculations based on household survey data

On the one hand, the difference in participation rate between the 1st and

the 5th

income quintile increased from 13.3 percentage points in 1994 to

40.5 percentage points in 2005, and then began to fall, reaching 30.5

percentage points in 2008. On the other hand, if one considers a ratio of

probabilities for studying at the tertiary level for the 1st and 5

th income

quintiles, it decreased from 6% in 1994 to 2.5% in 2008 (through 3.7 in

2005).

28. After 10 years of rapid growth, the demand for higher education

among the most affluent households stabilized, possibly indicating

the end of adjustment caused by the economic transformation. The

enrollment rates within the three highest income quintiles were increasing

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

19

94

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95

19

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19

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99

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08

5th

4th

3rd

2nd

1st

25

from the beginning of the 1990s until the mid-2000s, then began falling

(see Figure 9). It is possible that high demand for labor in the fast

developing Polish economy, reaching its business cycle peak in 2006–

2007, lowered the motivation to enter tertiary school. Interestingly,

however, an enrollment rate drop was not observable for the households

within the lowest income quartile, who continued to improve their

participation in tertiary education until 2008. It seems probable, therefore,

that the stabilization of enrollment rates for particular quartiles results

from reaching the “natural level,” rather than from the high demand for

less skilled workforce in the peaking economy.

29. The expansion of tertiary schooling in medium-sized cities

undoubtedly shortened the way to higher education institutions for

secondary school graduates, reducing spatial inequity in access. One

important aspect of tertiary schooling development in Poland relied on

the establishment of a number of tertiary institutions outside the largest

metropolitan areas, in cities of regional importance or even smaller. In the

context of the low mobility of Polish students and the very low capacity

of student dormitories (according to the Central Statistical Office, only

6% of tertiary students live in dormitories), reducing the distance between

tertiary school and households should significantly improve the

accessibility of tertiary studies in the provincial areas of Poland.

Figure 10. Participation in tertiary education for persons ages 19–26 years, by

person‟s place of residence (1994–2008)

Source: Author‟s calculations based on household survey data

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

500,000 and more200,000 - 500,000100,000 - 200,00020,000 - 100,000less than 20,000rural

26

Research based on the data from the web portal tracking the educational

careers of students showed that the average distance between the

secondary school from which an individual graduates and the tertiary

school he or she chooses to attend has decreased from 79 km in 1990 to

67 km in 2008 (Herbst 2009). Also, household data (Figure 10) prove

that spatial accessibility to tertiary education improved vastly in the past

13 years. For instance, the enrollment rate for individuals living in rural

areas increased from 3% in 1995 to 24% in 2008.

30. Participation in tertiary education improved for both those

individuals with high- and with low-family educational backgrounds,

but since the 1999–2000 school year the gap in enrollment rate

between these two groups has begun to close. Previous studies on the

equity of the Polish tertiary education system emphasized the role of

parental education in determining tertiary school accessibility. Indeed, as

shown in Figure 11, 50% of individuals with a father holding a degree

entered tertiary school in 1994, as compared to only 3% of individuals

whose fathers had reached only the primary level of education. During

the 1990s, the difference in higher-education participation rates between

families with highly and poorly educated parents was increasing, despite

the fact that accessibility was generally improving across all social

groups. In 1999, the enrollment rate for the individuals with a degree-

holding father reached 72%, and it remained close to 70% during 1999–

2008, which suggests it met a demand frontier at this level.

27

Figure 11. Participation in tertiary education for persons ages 19–26 years, by

father‟s education level (1994–2008)

Source: Author‟s calculations based on household survey data

Similarly, the enrollment rate for households with fathers having

secondary education was improving until the mid-2000s and eventually

stabilized around 50%. The participation rates among the least-educated

families were, in turn, rising during 1994–2008 and eventually reached

30% and 17% for those with fathers holding basic vocational and primary

education, respectively.

31. The proportion of persons ages 19–26 years with blue-collar family

backgrounds enrolled in tertiary school grew rapidly in the 1990s

and 2000s, but still remains 15% below that for unskilled white-collar

families and almost 30% below that for high-skilled white-collar

families. The father‟s occupation is also considered an important

predictor of the individual‟s educational career. An ISCO-88

occupational classification applied in a Polish household survey allows

the grouping of occupations into four broad categories: high-skilled white

collar, low-skilled white collar, high-skilled blue collar, and low-skilled

blue collar. The Polish data show no difference in tertiary education

participation between the families with a father having a high-skilled

blue-collar and those with a low-skilled blue-collar job (see Figure 12).

The difference between the rates for blue-collar and white-collar families

is around 15 percentage points and seems stable over time. In turn, the

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

19

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08

tertiary

secondary

basic vocational

primary

28

gap between low-skilled and high-skilled white-collar families is close to

20 percentage points, decreasing slightly in the most recent years.

Figure 12. Participation in tertiary education for persons ages 19–26 years, by

father‟s occupation (1997–2008)

Source: Author‟s calculations based on household survey data

Full-time versus part-time students. Who they are.

32. Although the statistical relationship between the level of income and

the probability of part-time studies is negligible, it is probably caused

by “reversed causation.” There is no doubt that attending full-time

programs is easier for more affluent students, but as all part-time

programs require a tuition fee to be paid, a vast majority of part-

time students work, which in turn increases their income. As a result,

in recent years the proportions of students ages 19–26 years in

nonstationary or evening programs are almost equal between income

quintiles and remain close to 40% (see Figure 13).

.0

.1

.2

.3

.4

.5

.6

.7

high skill blue collar

high skill white collar

low skill blue collar

low skill white collar

29

Figure 13. Students ages 19–26 years, in part-time programs, by income quintile

(1997–2008)

Source: Author‟s calculations based on household survey data

It is essential, however, to realize what the important factors are that may

depress the share of affluent students and increase the proportion of less

privileged individuals attending part-time programs. First, students from

the least endowed families need to finance their studies (that implies

some costs paid with mostly their own money, which implies combining

school and labor market activities. In turn, this prevents low-income

people from even applying to full-time programs. Moreover, the

distribution of households between income quintiles overlaps to some

extent with the differences in educational attainment of their members.

Students with good family-educational backgrounds tend to win the

competition for the tuition-free spots in full-time public schools, which

decreases their proportion in the part-time category.

33. Students with low family-educational backgrounds are much more

likely to end up in part-time tertiary programs than are their peers

coming from well-educated households. Interestingly, despite

revolutionary changes in the Polish higher education system, the scale of

sorting full-time and part-time students as conditional on family

background had not changed over the 10-year-period of 1997–2008 (see

Figure 14).

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

1

2

3

4

5

30

Figure 14. Students ages 19–26 years in part-time programs, by father‟s

education (1997–2008)

Source: Author‟s calculations based on household survey data

Only 20% of students with a father holding a university degree would

study in a part-time program. About 60% of tertiary students with only a

primary educational background and 50% of those with fathers who had

obtained basic vocational education, however, would study in a part-time

program.

34. Part-time studies are chosen (voluntarily or for necessity) by 50% of

the students living in small- and medium-sized towns and villages.

This is 20 percentage points more than in the case of students from large

cities. Interestingly, the propensity to study part-time divides students

into two groups: those who live in settlements with populations of more

than, and those who live in settlements with populations of fewer than,

100,000. No significant variation in the proportion of part-time students is

observed within these groups, indicating that the population of 100,000

constitutes a threshold, above which it is possible to study “locally” in

Poland (see Figure 15).

.0

.1

.2

.3

.4

.5

.6

.7

primary

basic vocational

secondary

tertiary

31

Figure 15. Students ages 19–26 years in part-time programs, by person‟s place

of residence (1997–2008) Source: Author‟s calculations based on household survey data

Unfortunately, the data do not allow classification of students according

to the size of their home towns before they entered tertiary school, but

only by their place of residence while studying.

For whom have the private tertiary institutions emerged?

35. While, in the past, the number of private6 schools chosen by students

with different levels of income were similar in proportion, recently

they became more popular among the most affluent students.

Household survey data allow us to estimate the proportion of students,

among different social groups, choosing to attend public versus nonpublic

tertiary institutions. Although necessary information on the type of school

(public, nonpublic) has been acquired in the survey only since 2001, it

still provides valuable information on the trends in school choices. At the

beginning of the 2000s there was no significant difference between the

proportions of students in nonpublic tertiary schools between different

income quintiles. The indicator varied from 15% to 19%, with slightly

higher values observed for more affluent families. Over the following

years, however, the propensity to choose private school was increasing

among the students from the richest quintile, eventually reaching 24.5%

6 The terms “private” and “nonpublic” have the same meaning in this report, denoting schools that are not

operated by state or territorial self-government.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

500,000 and more

200,000 - 500,000

100,000 - 200,000

20,000 - 100,000

less than 20,000

rural

32

in 2008, while remaining relatively stable for all the other income levels

(see Figure 16).

Figure 16. Students ages 19–26 years in nonpublic tertiary schools, by quintiles

of household revenues per capita (2001–2008)

Source: Author‟s calculations based on household survey data

36. There is no doubt that attending public versus nonpublic school is

conditioned by the student’s educational background. During the

2000s the proportion of students in private tertiary schools among those

whose father held a university degree was about two times smaller than

for students with a family background at only a primary educational level.

For instance, the respective indicators in 2005 were 13% and 22% (see

Figure 17).

Figure 17. Students ages 19–26 years in nonpublic tertiary schools, by father‟s

education (2001–2008)

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

2001 2002 2003 2004 2005 2006 2007 2008

1

2

3

4

5

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

2001 2002 2003 2004 2005 2006 2007 2008

primary

basic vocational

secondary

tertiary

33

Source: Author‟s calculations based on household survey data

Studying in nonpublic institutions is also more likely in the case of

students with fathers holding a low-skilled blue-collar job (28%) than for

those with a white-collar background (21%). There is no clear evidence,

however, that the place of residence (and particularly its size) affects

students choosing between public and private institutions.

Who pays for attending tertiary program?

37. It is only in the last three years that tertiary programs requiring

tuition fees are becoming more frequently attended by the high

income students than by the less affluent ones. Nonpublic programs

are not the only type of tertiary education that requires student paying a

tuition fee. The fees are collected also from the students of publicly

owned schools attending evening and nonstationary courses. Combining

information on public versus nonpublic, and stationary versus evening or

nonstationary, programs allows us to distinguish between those students

who benefit from free higher education and those who pay for attending

schools. Interestingly, as shown in Figure 18, in the early 2000s the

tracking between paid and free programs didn‟t seem to be determined by

family income. The proportions of students paying tuition fees remained

between 45% and 50% for all income quintiles. Only in the most recent

years (2006–2008) have the paid programs become slightly more

common for most affluent students (50% in 2008) than for those in the

poorest quintile (41%).

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

2001 2002 2003 2004 2005 2006 2007 2008

1

2

3

4

5

34

Figure 18. Students ages 19–26 years attending paid tertiary programs, by

quintiles of household revenues per capita (2001–2008) Source: Author‟s calculations based on household survey data

38. Students with high-education family backgrounds seem to win the

competition for the available slots in free tertiary programs. Less than

30% of students with a father with a university degree attend a program

requiring a tuition fee. In turn, almost 70% of students with the poorest

background (the father received only a primary education) have to pay

directly for tertiary schooling. The same applies to 56% of students

whose fathers hold basic vocational training (see Figure 19).

Figure 19. Students ages 19–26 years attending paid tertiary programs, by

father‟s education level (2001–2008) Source: Author‟s calculations based on household survey data

Whether or not a student is obliged to pay for a tertiary program is also

influenced by his or her father‟s occupation, although not as strongly as

by educational background. For students from the high-skilled white-

collar families (according to ISCO 88 classification) about 33% end up in

paid tertiary programs, as do about 45% of students from the low-skilled

white-collar background. For the blue-collar families the ratio exceeds

50%, with no meaningful difference between high- and low-skilled

occupations of fathers.

Logistic model of tertiary school accessibility

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

2001 2002 2003 2004 2005 2006 2007 2008

primary

basic vocational

secondary

tertiary

35

39. Analyzing separately the impact of different aspects of socioeconomic

stratification on the accessibility of higher education does not necessarily

allow assessing the real importance of particular factors. Various family

characteristics overlap, so the measures of inequality are correlated, even

if describing different features of students‟ families. The simple rates of

participation in higher education calculated for different socal groups can

not give a precise assessment of the factors‟ significance. To solve this

problem, we will try to estimate the role of different factors in

determining the probability of entering higher education within a model

comprising selected student and family characteristics. The estimation

will be based on logit procedure, which allows measuring the impact of

several explanatory variables about the chance of attending tertiary

school. More precisely, if we consider pi to be the student‟s i probability

of entering tertiary education, then the variable explained in logit

specification will be defined as the natural logarithm of the odds ratio:

(pi)/(1-pi). The details of model specification, as well as the full results,

are shown in the Appendix.

40. Concerning the difference by sex in participation in tertiary

education, the estimation confirmed that in the 1990s and early 2000s

the probability of entering the tertiary school was significantly higher

for females than for males. The value of odds ratio (females to males)

remained close to 2.00 between 2000 and 2006, but in the most recent

period (2007–2008) it began to fall, indicating a decreasing gap between

the probability of studying, for males and females.

41. Father’s education remains the strongest determinant of tertiary

education participation, but its importance gradually decreased

between 1995 and 2008. In the mid-1990s, the odds for individuals with

a tertiary-education family background was more than 7 times higher than

for those whose fathers received only primary education. By 2008 this

ratio fell to 4. The advantage provided by a father with secondary

education (as compared to primary education) was substantially smaller,

but also was more stable over time. In 1995 it exceeded 2.5, and by 2008

it decreased to 2.0. The probability of participation in tertiary education

for the individuals with a basic-vocational family background is, in turn,

lower than for those having fathers with only primary education. The

odds ratio for these least-educated types of families has not changed

significantly over time.

36

Figure 20. Odds ratios for access to higher education, by father‟s educational

attainment.

Note: Estimated through logistic regression. Primary education is the reference

category. Source: Author‟s calculations based on household survey data

42. Independently of educational background, the household income

seems to affect the chances of entering higher education. With lowest

income quintile being a reference category, the odds for the remaining

quintiles in 2008 are 1.28, 1.57, 1.71, and 1.85 respectively. Thus,

although the impact of income on the probability of studying is

significant, it remains substantially weaker than that of father‟s

educational achievement. The influence of income was also changing

over time in different ways from the effect of family educational

background.

0

1

2

3

4

5

6

7

8

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

father's higher education

father's secondary education

father's basic vocational education

37

Figure 21. Odds ratios for access to higher education, by income level.

Note: Estimated through logistic regression. First quintile is the reference

category. Source: Author‟s calculations based on household survey data

Between the mid-1990s and 2002 the role of income in determining

participation in tertiary education was increasing. The odds within the

most affluent income quintile, as compared with the poorest, reached 3.4.

Eventually the impact of income weakened, and as of 2008 the odds

values for particular income quintiles are not very different from these

observed in 1995.

43. By the end of the 1990s, the distance to a large city ceased to be an

unbreakable barrier to accessing a tertiary education program. In the

middle of the decade the residents of the metropolises faced a respective

odds ratio about four times higher than did those living in rural areas.

Recently this indicator dropped to approximately 1.5 (see Figure 22).

Although some difference in accessibility of tertiary education in

medium-sized towns (with a population of up to 100,000) as compared to

the largest cites is observed, it is also, in this case, just a fraction of the

spatial inequity existing 15 years ago, and the gap is no longer

substantial.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

income quintile 2

income quintile 3

income quintile 4

income quintile 5

38

Figure 22. Odds ratios for access to higher education, by place of residence.

Note: Estimated through logistic regression. Rural areas are the reference

category. Source: Author‟s calculations based on household survey data

Equity of access to higher education in Poland versus other European

countries

44. The impact of parents' socioeconomic status on student participation in

tertiary education was tested by the Eurostudent survey conducted 2005–

2007 in 22 European countries. Although Poland was not covered by this

research, some comparative indicators could be obtained using data from

the Household Budget Survey (see Table 4). The general conclusion from

the Eurostudent research report (Eurostudent 2008) is that student

performance is linked to their socioeconomic status, therefore adequate

intervention is needed at the earlier stages of education to correct such

disadvantages. Currently most countries recruit proportionally more

students with white-collar backgrounds, and in all researched countries

students whose fathers attained higher education are overrepresented.

Duration of higher degree programs, type of degree students pursue and

existence of nonuniversity institutions help to explain the participation of

less affluent students. Providing a good quality education across all

schools (especially at lower tiers) is important to have more students from

less affluent backgrounds participating in higher education.

45. Similarly to the rest of Europe, Poland faces an overrepresentation of

students with high socioeconomic status in tertiary schools, but

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

500,000 and more

200,000 - 500,000

100,000 - 200,000

20,000 - 100,000

less than 20,000

39

Poland seems to have more equitable access to higher education than

in most European countries. For instance, the ratio of Poland‟s

proportion of students‟ fathers holding highest educational attainment

compared to a similar proportion in the general population (of

corresponding age) equaled 1.56 in 2008. This is more than the value

reported by Eurostudent for Finland, the Netherlands, Norway, and

Switzerland (suggesting that the system is more equitable in these

countries than in Poland), but substantially less than that in France,

Austria, the countries of Southern Europe and the whole Eastern

European region.

Country ISCED 5-6 level

completed

ISCED 0-2 level

completed

Blue-collar

occupational status

POLAND

(2008) 1.56 0.55 0.88

Finland 1.43 0.96 0.92

Netherlands 1.37 1.04 0.79

Slovenia 1.52 0.59 0.78

Scotland 1.21 n.d. 0.72

Turkey 2.53 0.49 0.71

England/Wales 1.9 n.d. 0.69

Sweden 1.77 0.66 0.68

Romania 3.01 0.2 0.67

Ireland 1.53 0.81 0.64

Italy 1.75 0.59 0.63

Estonia 2.39 0.42 0.63

Portugal 3.01 0.71 0.59

Denmark 2.03 0.4 0.58

Slovak

Republic 2.11 0.27 0.55

Czech Republic 2.15 0.21 0.53

France 2.29 0.6 0.49

Austria 1.91 0.7 0.47

Spain 1.63 1.03 0.37

Latvia 2.15 0.29 0.33

Bulgaria 3.05 0.09 0.27

Switzerland 1.36 0.9 n.d.

Norway 1.45 0.58 n.d.

Table 4. Students‟ fathers with particular educational attainment or occupational

status, ratio to corresponding proportion in general population (males ages 40–

60 years), 2005–2007 Source: Based on: Eurostudent 2008.

40

Note: n.d. – no data; Ratio value exceeding 1 indicates overrepresentation of a given group of

students, while less than 1 indicates underrepresentation.

Data for Poland: Author‟s calculations based on Household Budget Survey 2008

46. Regarding inequity measured by the underrepresentation of students

with very low educational background in tertiary schooling, Poland

is in the middle of the European ranking. The most equitable systems

are developed in Finland, the Netherlands, and, surprisingly, Spain.

Accessing higher education is most difficult for the students with low

family-educational background in Bulgaria, Latvia, Romania and the

Czech Republic. In turn, coming from a blue-collar environment doesn‟t

significantly depress an individual‟s chances of admission to tertiary

schools in Poland, compared to other European countries. Out of 22

higher education systems only the Finnish system proved to be more

equitable.

A demographic factor

47. Changes in accessibility to tertiary education in Poland, and

particularly in accessibility to tuition-free programs may be related with

the lowering demographic pressure on the system. Demographic decline

in the ages 19–24 years cohort in Poland started in 2005. It is predicted

that by 2025 the size of this cohort will decrease by 1,494 thousands (or

41.2%), compared to 2008. Between 2025 and 2035 the trend will be

reversed, with 13,2% growth expected during this period (Główny Urząd

Statystyczny 2009). The projected downturn will exert a strong influence

on the tertiary education system in Poland – if the enrollment ratio is

sustained, the number of students in Poland will decrease by 762

thousands (nearly 40%) between 2008 and 2025. It is expected to entail

the closing of many TEIs and diminishing income from tuition fees

(Sztanderska 2009).

41

Figure 23. Current and projected population for the ages 19–24 years cohort in

Poland. Based on: (Główny Urząd Statystyczny 2009).

Demographic decline will inevitably result in tougher competition for students

between tertiary schools. From the equity point of view this competition may

have plausible effects, as it may contribute to improving the chances of students

from underprivileged social groups to attain higher education. Some of the

recent drop in the impact of income level on the probability of studying,

demonstrated earlier in this report, is likely to be caused by this mechanism

already.

Quality concerns – hidden inequity in tertiary education?

48. The recent developments in the Polish higher education sector have

shifted the focus of equity related debate from the issue of service

accessibility to the differences in quality of education programs

offered to different social groups. An often raised issue in the context of

the rapid development of tertiary education in Poland is the relationship

between the access to education and its quality. There is no doubt that

allowing mass enrollment in higher education institutions must have

resulted in lowering the average performance of the graduates. Even if the

quality of tertiary schools, measured for example by the resources

available per student, would remain unchanged (which was not the case,

0

500

1000

1500

2000

2500

3000

3500

4000

[in th.]Current and projected demographics of the age cohort 19-24 in

Poland

rural areas

urban areas

42

as shown below), the average level of skills with which the candidates

enter the tertiary level has still decreased significantly. Before

transformation, tertiary education was the privilege of the intellectual

elite. Now, it has become a generally accessible good. The drop in quality

must be considered an inevitable cost of widened access to education.

Although it is important to investigate the scale of how much the average

quality loss results from increased enrollment, what is crucial from the

equity perspective is to understand the differences in quality between the

schools and programs accessible for various social groups. It seems likely

that, due to market segmentation, the most problematic aspect of inequity

in the tertiary education system does not involve the access of students

from underprivileged environments to tertiary education in general, but

the probability of their admittance to the programs of good quality.

49. Measuring the quality of tertiary education is difficult. Unlike at the

primary or secondary tiers, the achievements of students at this level are

not assessed by any standardized tests, which would be ineffective given

the variety of specializations. There are two main approaches to

measuring the quality of tertiary school. One is to look at the resources

available for schooling. This includes money, but also time devoted to

teaching, teacher statistics and availability of specific infrastructure. In

the case of Poland, it is of interest how the availability of resources

changed over time for the tertiary schooling sector as a whole, but also

what the differences are between public and nonpublic schools. The other

way to assess the quality of tertiary schooling relies on analyzing the

impact of higher education on labor market performance of the graduates.

This kind of study requires a careful methodological approach. The labor

market achievements (both probability of employment, and wages

earned) depend on several characteristics of a graduate, and isolating the

effect of school quality is difficult. As the available data for Poland do

not allow measuring the quality of education by labor market outcomes,

in the present study we focus on resources as indicators of educational

quality. However, we also report some important conclusions from the

labor market studies performed by other researchers in the following

section.

43

Quality of tertiary schooled: related to available resources

50. The first sign of the lowering quality of tertiary education in Poland

was the shift from full-time to part-time programs as a common

mode of studying. According to numerous studies, the quality of

part-time programs is, on average, weaker than that of full-time

programs. The proportion of full-time students decreased from 77% in

1990 to 48% in 2008 (see Table 5). The development of the nonpublic

sector in education played a particularly important role in managing this

drop – in 2008/2009 nearly 54% of part-time students were enrolled in

the nonpublic sector (Główny Urząd Statystyczny 2009). There are many

reasons to believe that part-time programs provide students with a lower

quality education as compared to full-time programs. Some research

shows that teaching takes place in overcrowded conditions, there is

insufficient time for direct student-teacher relations, , the profiles often

mismatch the needs of the labor market, etc. (United Nations

Development Programme 2007); (Drogosz-Zabłocka and Minkiewicz

2007). The teaching time offered to students in part-time programs prior

to 2005 could be even 65% to 70% less than that of the respective full-

time programs. In 2003, new regulation was introduced setting a

minimum duration for part-time studies to 50% of the respective full-time

program (80% in the case of evening programs). As shown above, part-

time programs are chosen more frequently by the students with low-

education family backgrounds, and also (unsurprisingly) by those living

outside large cities. This means that students from underprivileged social

groups may receive lower quality educations than do their more

privileged peers.

44

1990

2008

Total Public Nonpublic

Enrollment ratio 9.8 40.6 - -

% of full-time students 77.2 48.1 63.7 18.3

Student/teacher ratio 6.6 18.9 15.1 37.3

SCImago Country Rank 16 20 - -

Contribution to scientific potential of Poland - 100% 98.5% 1.5%

Public spending per student (in PLN) - 8,564 11,123 3642

% of teachers in first post (public school only) 76.8* 63.0 74.8 5.5

% of students receiving nonrepayable support 54.3 24.0 25.5 21.3

Distribution of students by fields of study (%)

Education 14.2 11.8 9.9 15.5

Social science 4.4 13.5 12.2 16.0

Business and administration 13.2 23.5 16.8 36.5

Engineering 17.2 6.9 10.3 0.3

Health 10.1 6.1 7.0 4.5

Mathematics & computing 3.1 5.4 5.3 5.6

Science n.d. 8.9 10.2 6.4

Table 5. Characteristics of Polish higher education system in 1990 and 2008. Source: Based on (Główny Urząd Statystyczny 2009); (GUS 1991); (GUS 2000); (Bajerski 2009);

(Ernst&Young and Instytut Badań nad Gospodarką Rynkową 2009).

Note: n.d. – no data. Data on SCImago country rank for 1996 and 2008. Data on contribution to scientific potential for 2006. Data on percent of teachers in first post for 1993 and 2008. *Based on a survey of a representative group of academic teachers undertaken in 1993 by Wnuk-Lipińska

(Wnuk-Lipińska 1996)

51. Another important aspect of the changes in the Polish tertiary

education system involves the dominant position of the humanities

and social sciences. Most of the new slots created in higher education

during the 1990s and 2000s were funded directly by tuition fees, with

little financial aid from the state. This resulted in a shift toward mass,

inexpensive studies (see Table 5). The distribution of students by profiles

reflects their willingness to acquire a tertiary school diploma at low cost,

and not necessarily a concern about the demand for various professions

by the labor market. The data on unemployment among tertiary school

graduates are not systematically collected at the country level, but

regional statistics prove that there is a mismatch between the distribution

of students by faculty and the needs of the labor market. For instance, in

the Opolskie region, the unemployment rate by the end of 2007 among

recently graduated economists was 14%, while for the graduates of

regional technical university (politechnika) it did not exceed 2–3%. The

45

general unemployment rate in the region at this time was 12%

(Wojewódzki Urząd Pracy w Opolu 2009) .

As shown in Table 5, the current distribution of Polish students among

academic profiles skews strongly toward social sciences, business, and

pedagogy. This becomes particularly noticeable when compared to the

corresponding proportions in EU27, where one can observe a notably

higher proportion of students in engineering and science (Kancelaria

Prezesa Rady Ministrów RP 2008). When we focus our attention on

nonpublic institutions, the predominance of “cheap” faculties is even

stronger.

52. Along with the increase of enrollment in higher education, the

student teacher ratio, commonly used in the educational research

literature as an indicator of quality, also increased dramatically. The

number of full-time academic teaching positions in Polish tertiary schools

amounted to 99,0007 in the academic year 2008/2009, thus grew by 60%

since 1990 (Główny Urząd Statystyczny 2009),(Ernst&Young and

Instytut Badań nad Gospodarką Rynkową 2009) It is a moderate increase

compared to a fivefold growth of enrollment during the last 20 years. As

a consequence, the accessibility to teachers has significantly worsened for

students. A closer look at the staff-student ratio reveals significant

differences according to the field of study. The ratio ranges from 5 in the

case of Agriculture, Health & Welfare, and Science, through 37 in Social

Science, to 67 in Education. These differences are even more striking

when we pay attention to professors only. A student-professor ratio

ranges from 23 in Agricultural faculties, through 307 in Social Science, to

920 in Education. Furthermore, 29% of professors employed in Polish

higher education institutions are more than 70 years old (Ernst&Young

and Instytut Badań nad Gospodarką Rynkową 2009). According to data

from 2007, there were 18.9 students per teacher in tertiary education,

compared to 15.3 on average in the OECD (OECD 2009). Within the

Polish system, we noticed a significant difference between public schools

(15 students per teacher according to the latest data) and nonpublic ones

(37).

7 This value refers to number of posts, therefore an individual employed at two full-time posts simultaneously is

counted twice.

46

53. Enrolling so many students with such scarce human resources

wouldn’t be possible without multiple employments of staff, which

severely compromises the quality of teaching, particularly in

nonpublic institutions, which are often “second posts.” Some 30% of

Polish academic staff, and 66% of Polish professors hold multiple

teaching posts, either in other tertiary institutions or outside education

systems (Ernst&Young and Instytut Badań nad Gospodarką Rynkową

2009). Multiple employment is a particular impairment at nonpublic

schools – 95% of academic teachers work there on their (at the best)

second post, compared to 25% in the case of public institutions (Główny

Urząd Statystyczny 2009). Such academics have to share their teaching

time between at least two schools, which undoubtedly lowers their ability

to match students‟ specific needs. Moreover, such professors are unlikely

to devote much time to the needs of students in nonpublic institutions,

who are de facto directly paying for their courses, while nonpaying

students tend to get priority attention, as they are academically stronger

(passing the merit-based selection process). Therefore, the less-well-off

students attending nonpublic TEIs are indirectly subsidizing the better-off

students in public universities (World Bank 2004).

54. The performance of Polish higher education institutions in the area

of academic research, correlated with the quality of teaching, is not

improving along with the expansion of tertiary schooling in Poland,

and the contribution of nonpublic schools to the internationally

recognized literature is negligible. According to SCImago Journal &

Country,8 Poland‟s rank (by number of publications) decreased by 4

places since 1996, giving Poland 20th

place in the world in 2008. If

focusing on the citation indicator reflecting the quality of the

contribution, Poland drops to 38th

place out of 68 countries included in

this query (Ernst&Young and Instytut Badań nad Gospodarką Rynkową

2009). According to the standard assessment of the academic potential of

Poland, undertaken in 2006 by State Committee for Scientific Research,

98.5% of this capability (measured by, e.g., number of publications,

research grants, patents obtained, etc.) lies in the public sector, while only

1.5% result from research activities performed by nonpublic schools.

8 SCImago Journal & Country Rank is a bibliometric project of 4 Spanish TEIs, which provides international

comparisons based on number of publications and citations.

47

The quality of tertiary education and graduates’ labor market performance

55. It is widely accepted that outcomes of tertiary education are

measured through a series of indicators describing the labor market

situation for graduates, but the Polish statistical system lacks this

kind of information. Measuring the outcomes of tertiary education

should correspond to the goals set for this education level. Although a

school‟s aim is not just to prepare graduates to enter the labor market, this

is the factor that is indisputably measurable. Surveying the matching of

graduates‟ skills to employers‟ needs hasn‟t developed well in Poland.

Not only is it not systematically approached by public statistics, but it is

also commonly ignored by the TEIs in Poland, who pay little attention to

the labor market careers of their former students. This section presents

some conclusions from the reports that are typically based on unique

surveys or data that are occasionally published by public employment

services. These reports don‟t provide a complete picture of how tertiary

schooling quality affects graduates‟ professional careers, but they still

contain some useful evidence.

56. Although simple statistical comparisons show that attaining higher

education increases the likelihood of finding jobs, note that the

measures usually applied are subject to strong selection bias. Up until

the year 2000, more than 90% of individuals with tertiary attainment had

managed to find a job during the 3 years after graduation. In 2005 this

number decreased to 80.3%, possibly indicating a growing mismatch

between the quality of the tertiary programs and the employers‟

expectations, or an oversupply of tertiary graduates on the Polish labor

market (Grotkowska and Sztanderska 2007). Naturally, a higher

probability of being employed after tertiary education should be

attributed not only to higher education attainment, but also to the program

selection process by students prior to enrolling in a tertiary school.

Similarly, the decreasing probability of graduates finding jobs may reflect

a lowering of the selection criteria, as well as a worsening of the quality

of teaching. Nonetheless, most of the studies published so far find strong

evidence that holding a tertiary education degree improves labor market

performance. For instance, according to the research conducted in 2007,

the employed to unemployed ratio among graduates holding a university

degree is 9.6 compared to 3.1 for basic vocational school leavers and 3.9

among individuals with secondary vocational education (Sztanderska,

48

Grotkowska et al. 2007). The evidence also proves the advantageous

status of university degree holders compared to bachelor‟s degree

holders.

Labor market status of 1998–2005 graduates from different levels of education

Level of education attained Employed

(%)

Unemployed

(%)

Employed to

unemployed

ratio

Upper secondary: Basic vocational 63.7 20.7 3.1

General path 55.7 19.1 2.9

Vocational 68.7 17.8 3.9

Postsecondary nontertiary 70,9 15.4 4.6

Tertiary: Bachelor/engineer 75.1 12.4 6.0

Master 84.7 8.8 9.6

Table 6. Labor market status of 1998–2005 graduates from different levels of

education

Source: (Sztanderska, Grotkowska et al. 2007)

57. The importance of tertiary education for labor market performance

is also confirmed by the results of probit model estimations. The

advantage of this approach over simple comparisons between individuals

with different education levels is that it simultaneously takes into account

the influence of various determinants of labor market status, and not just

educational attainment. By doing so, the model controls for the selection

bias resulting from the fact that tertiary school graduates are better

endowed with skills valued by the labor market even before they reach

the higher education tier since, on average as compared with other social

groups, they come from more highly educated and more affluent families,

they graduated from better primary and secondary schools, etc. The

estimation shows that achieving a university degree improves the

probability of employment by 13 percentage points as compared with the

individuals holding basic vocational education. However, the effect is

much stronger for females (20 percentage points) than for males (3

percentage points), The results show also that only females gain some

advantage in their chances of employment by achieving a bachelor

degree, while for males this effect is small and negative (Sztanderska,

Grotkowska et al. 2007).

49

Finally, according to the research by Sztanderska and Wojciechowski

(Sztanderska and Wojciechowski 2008) higher education degrees lead to

an hourly wage that is higher by 70% than the salary obtained by

individuals with only primary education, and the difference between

higher and secondary education premium is 40 percentage points.

58. Due to unavailability of data there has been no fully reliable Polish

research on the consequences of attending private versus public

tertiary school, and a full-time versus part-time program, for the

labor market performance of the graduates. The studies done so far

found no significant difference in chances to find a job between students

that graduated from public and nonpublic schools (see Table 6).

However, in the absence of appropriate data these studies have ignored

(although they mention it) an obvious selection bias involved in the

comparisons between different sectors and programs.

Table 7. Labor market status of graduates by type of TEI and mode of study,

2008. Source: (Ernst&Young and Instytut Badań nad Gospodarką Rynkową 2009)

In particular, since the nonstationary programs, as well as full-time

studies in nonpublic schools, require paying tuition fees, it is necessary

for many students to be employed even before applying for admission, or

at least at much a earlier stage of study than are their peers in full-time

public programs. Thus, employment becomes a necessary condition for

school admission and not the effect of the degree achieved later.

50

59. A number of attempts were made to measure quality of tertiary

education through opinion surveys of students, graduates and

chancellors. They reveal substantial differences between the typical

profiles of public and nonpublic institutions. A study on graduates‟

skills and abilities obtained during their tertiary education, according to

school chancellors, was conducted in 2004 (Sztanderska et al. 2004). The

results presented in Table 8 suggest that public schools have better

learning outcomes in advanced IT skills, knowledge of humanities and

new trends in technology, while graduates of nonpublic schools are better

equipped with work experience and knowledge of new trends in

management. However, note that these relative advantages might be

attributed to distinct field-of-study structure and the prevalence of part-

time mode among these two sectors.

Skills Public TEIs (%) Nonpublic TEIs (%)

Advanced foreign language skills 25 20

Advanced IT skills 31 23

Knowledge of new trends in management 47 70

Knowledge of new trends in technology 56 30

Basic knowledge of humanities 66 50

Work experience 59 80

Supplementary trainings 41 43

Social responsibility 56 63

Abilities

Creativity 50 53

Cooperation 63 60

Self-learning 81 40

Using theory in practice 59 66

Table 8. Skills and abilities obtained by public and nonpublic students through

tertiary education, as perceived by TEI chancellors (2004). Source: Sztanderska et al. 2004.

60. According to a survey of studentsin the Wielkopolskie region,

graduates from public TEIs feel better equipped with general

theoretical knowledge and self-learning ability (which corroborates the

above-mentioned findings). Conversely, nonpublic TEIs offer more

practical knowledge and useful IT skills, and they teach how to solve

problems independently. However, these opinions are shaped towards

expectations, which may differ notably among students of various types

of TEIs.

51

% answers strongly agree and agree divided by % answers strongly disagree and disagree Public TEIs Nonpublic TEIs

General theoretical knowledge of the discipline studied 26.0 22.0 General practical knowledge of the discipline studied 1.4 1.6 Field-specific theoretical knowledge 3.0 4.1 Field-specific practical knowledge 1.0 1.1 Ability to use knowledge in practice 1.2 1.3 Ability to solve problems independently 3.7 5.9 IT skills (needed for future job) 1.3 3.2 Advanced foreign language skills 0.7 0.8 Self-learning ability 3.1 2.7

Table 9. Final-year students‟ opinions on skills and abilities gained during their

tertiary education – Wielkopolskie voivodeship. Source: Leszkowicz-Baczyński 2007.

61. Labor market outcomes notably favor master’s degree graduates

over bachelor’s degree graduates, whose employment probability

doesn’t significantly exceed those for graduates of lower levels of

education. The authors of BAZA (2007) closely examined graduates‟

situations in the Polish labor market. The study focused on the population

of 1998–2005 graduates9 of all levels of schools above lower-secondary,

and, of particular interest with respect to higher education, it

distinguished between bachelor‟s and master‟s graduates. Basic

employment indicators for graduates of different tertiary programs

(shown in Table 10, part A) highlight the disadvantaged situation of

graduates from full-time, public, and bachelor‟s degrees – in their case

employed to unemployed ratio equals only 4,6. Conversely, master‟s

degrees graduates (excluding public, part-time programs) are the best

performers – ratios exceed the value of 10. Relatively good labor market

outcomes of part-time graduates (especially among bachelors) are due to

combining work and studies, which helps avoid unemployment upon

graduation (BAZA 2007) Interestingly, probit analysis10

(Table 10, part

B) shows that the probability of finding employment for bachelor‟s

graduates doesn‟t significantly differ from their peers with lower levels of

9 Individuals who finished their education (in basic vocational, upper-secondary, postsecondary nontertiary or

tertiary school) between 1998 and 2005, who were not more than 27 years old at graduation and who did not

have a gap exceeding 12 months between the last two stages of education (BAZA 2007, p. 20). 10

The probit regression is for finding employment, controlling for various sociodemographic and geographic

factors. In the case of educational attainment, basic vocational school is the reference. The earlier work

experience is not included in the model. For more information, see BAZA (2007, pp. 134–140).

52

education (i.e., secondary, upper-secondary and postsecondary

nontertiary). Only a master‟s degree gives a significantly higher

probability of getting employed than it is observed for individuals with

basic vocational education. Note, however, that the latter result refers

only to the graduates not holding a job when leaving school – it refers to

finding employment after graduation and not to being employed. The

same research shows that bachelor‟s graduates are more often employed

during their time of study, compared to master‟s graduates.

A. Labor market status of tertiary

graduates by type of TEI and mode of study

Employed (%)

Unemployed (%)

Employed to unemployed ratio

Bachelor’s programs

public full-time 66,3 14,4 4,6

part-time 82,2 8,6 9,5

non-public full-time 71,8 8,9 8,1

part-time 81,1 8,7 9,3

Master’s programs

public full-time 83,6 7,8 10,7

part-time 82,4 10,6 7,8

non-public full-time 85,5 7,6 11,3

part-time 86,3 8,2 10,6

B. Chance of finding employment at graduation – by level of educational attainment

Probit regression – marginal effects (ref.: basic vocational) Bachelors Masters

Up to 6 months after graduation 0,0353 (p= 0,149) 0,0568 (p= 0,003)

Up to 12 months after graduation 0,0079 (p= 0,647) 0,0500 (p= 0,000)

Table 10. Labor market status of tertiary graduates – by type of TEI and mode

of study (1998–2005 graduates). Source: BAZA 2007.

62. Master’s degree graduates tend to obtain better jobs, and their work

is more often related to their field of study, compared to those with a

bachelor’s degree. The portion of graduates whose first job is “good

quality”11

is 4.1 percentage points higher for master‟s than for bachelor‟s

graduates (see Table 11). The gap between these two groups gets even

wider if measures of job-education match and mismatch are applied.

Approximately three-fourths of master‟s degrees graduates find their field

of study related to their job and that skills obtained during tertiary

education are useful for performing it; while in the case of bachelor‟s

graduates this share is roughly three-fifths. Furthermore, employment 11

Type B (better quality job) is defined as self-employment, permanent employment contract or contract

exceeding one year. Type A (worse quality job) consists of less stable forms of employment such as civil law

contract, probationary employment, contract not exceeding one year, etc. For more detailed information see

BAZA (2007, p.118).

53

found by master‟s graduates has better prospects of promotion; also,

master‟s graduates are less likely to be dismissed against their will,

regardless of job quality.

Graduates employed in a given category (%)

Bachelor’s graduates

Master’s graduates

Actual job closely relates to graduate’s field of study

58.0 72.4

Usefulness of knowledge/skills in performing first job

63.5 75.3

First job is good quality (type B) 56.6 60.7

Prospect of promotion in first job

Type A 62.6 63.1

Type B 69.7 74.1

Dismissed from first job against employee’s will

Type A (% of dismissed) 5.6 2.3

Type B (% of dismissed) 12.4 8.7

Table 11. Employment quality for 1998–2005 master‟s and bachelor‟s graduates Source: BAZA 2007.

63. Bachelor’s and master’s graduates have similar salary expectations

at graduation, but labor market outcomes result in a more significant

growth of expectations and later pay rise, in the case of master’s

graduates. Expected and obtained salaries are other useful measures of

education outcomes, apart from employment indicators. Salaries

anticipated upon graduation are similar for bachelor‟s and master‟s

graduates (see Table 12), but adjustments of expectations differ,

indicating a relatively better situation for master‟s graduates.

Surprisingly, initial pay is slightly higher on average for bachelor‟s

graduates (by 7 PLN), which might be linked to more self-employment

among this group or to work experience gained before graduation. In

turn, subsequent pay rises for master‟s graduates (by 46.4% on average in

better quality jobs) is explained by employers‟ attempts to retain valuable

workers (BAZA 2007).

54

Salary expectations at graduation Bachelor’s graduates

Master’s graduates

Average [PLN] 1,122 1,196

Median [PLN] 1,000 1,000

Adjustment of salary expectations when looking for first job (% of graduates)

Decline 38.5 35.2

Growth 8.9 12

Initial pay in the first job (net value)

Average [PLN] 1,045.4 1,038.4

Median [PLN] 800 890

Pay rise compared to initial pay (net values. %)

Job type A 12.6 13.1

Job type B 28.5 46.4

Table 12. Expected and actual salaries for 1998–2005 master‟s and bachelor‟s

graduates

Source: BAZA 2007.

Tertiary graduates of social and economic sciences seem to face

significant employment challenges in Poland. Survey-based data on the

labor market outcomes of different occupational groups (holding tertiary

education) are presented in Table 13. The group “nature science, health &

welfare” is characterized by a notably low unemployment rate and a short

period before finding job on graduation, but only one-third of graduates

had been employed just before obtaining a degree. In turn, the group of

“remainder professionals” (consisting mostly of economics and

management studies graduates) has the highest share of unemployed, but

at the same time more than two-thirds of its members had been employed

even before graduation. It seems that the labor market position of

graduates in the “remainder” group is weakened by oversupply, therefore

the “added value” of such studies in the context of labor market success is

minor. The State Labor Office‟s data from 2001 (the last year available)

corroborate these findings – 8 of the top 15 academic disciplines

according to unemployment rate were related to economic and financial

studies (Kryńska 2003).

Labor market outcomes Occupation

55

Table 13. Labor market outcomes by occupational groups (1998–2005

graduates). Source: BAZA 2007.

Note: Occupation for which a graduate is trained; categories in accordance with

the Classification of Occupations and Specializations used in Polish statistics.

According to BAZA 2007 these groups were composed mostly of graduates of

academic programs in: physics, mathematics, engineering – engineering,

manufacturing & construction, physical science, computer science; nature

science, health & welfare – health & welfare, life science, agriculture;

education – education, humanities, foreign language studies; remainder –

economics, management, social science (BAZA 2007, p. 84).

64. Graduates of humanities and education studies enjoy a wage

premium of 35% over those with general education, in the private

sector. Salaries are more diversified according to graduates’ field of

study in the private sector compared to the public sector. Varying

salaries of graduates of a certain discipline reflect the differences in

profitability of a given field of study. The private sector values the

knowledge and skills of humanities and education graduates (wage

premium 35% more than for general education), science and mathematics

(23% more), social science, economy and law (18% more), and

engineering (12% more). Public sector pay is less dispersed, and only

humanities graduates receive a significant wage premium – 12% more

than those with a general education (Sztanderska & Wojciechowski

2008).

Medium-sized occupational groups Physics, mathematics, engineering

Nature science, health & welfare

Education Remainder

Employed 82,6 83,5 79,8 79,6

Unemployed 9,8 5,8 9,9 10,6

Already employed at graduation 48,3 31,7 55,2 67,7

Average time between graduation and employment (months)

5,3 4,5 6,5 7,1

56

Conclusions

65. The analysis presented in this report, based on household survey data

covering the period 1994–2008, confirms that tertiary education

participation in Poland has significantly improved across all income

groups and for individuals with all kinds of family educational and

occupational backgrounds. When compared to other European

countries, the equity of access to higher education, as measured by some

standard indicators, is now better than average. With respect to

overrepresentation of students with the highest family educational

background in tertiary schools, Poland ranks worse than Finland,

Switzerland, the Netherlands or Norway (the countries with greatest

achievements in equity promotion), but better than Italy, France,

Denmark, England, and many other countries researched in the

Eurostudent survey. The underrepresentation in Polish tertiary schools of

students with fathers with blue-collar occupations is the second smallest

after Finland.

Still, this research, as well as some prior studies, raises some doubts

about the equity of higher education in Poland.

66. First, the conditions for admission vary between types of schools and

programs in a way that clearly discriminates and financially burdens

students from less privileged social strata. Those students, who qualify

for full-time programs offered by public schools, do not pay any tuition

fees. The cost of their education is covered by the Ministry of Science

and Higher Education in a form of subvention from the central budget to

the public higher education institutions. In turn, the students attending

nonpublic tertiary schools, as well as those enrolled in part-time (evening

or nonstationary) programs in the public institutions are obliged to pay

directly for their education. This research proves that individuals with

weaker family backgrounds, if entering tertiary education, are more likely

to attend one of its paid forms. Although Poland is considered a low

tuition fee country compared to other European nations, imposing greater

financial burden on underprivileged social groups is certainly an

inequitable solution. Moreover, since students enrolled in different

schools and programs frequently share educational resources (teachers

employed at multiple posts, buildings, infrastructure, teaching aids) we

observe students in paid programs, de facto, on average having lower

socioeconomic status, indirectly subsidizing the courses for those

studying in a tuition-free system.

57

67. Second, the financial aid from the state to the students is limited.

Although, according to official data 12% of students receive social grants,

the transferred amounts are insufficient to cover a significant part of

tuition fees or living costs. Also the student loan system has not

developed well in Poland. Currently it covers 10% of students, of which

80% attend public schools. According to experts, the amounts offered are

not sufficient to cover the realistic costs of living, and loans are not

offered on favorable terms.

68. The research proves that the probability of participation in higher

education in Poland still depends significantly on some of the

characteristics of the individual, as well as on his or her

socioeconomic status. The strength of this dependence has been

changing since the mid-1990s. In particular, the advantage from a strong

family educational background (its influence on the chance of studying)

decreased at a fast rate between 1995 and 2002 and eventually became

stable in the years that followed. However, although the impact of family

education on participation in higher education is now 50% lower than it

was in the mid-1990s, it still constitutes, of all factors examined, the most

important determinant of educational career.

69. The expansion of the higher education sector in Poland undoubtedly

led to a reduction of spatial inequity in access to tertiary schooling.

Looking back to the mid-1990s, we see that one‟s chances of studying

were highly conditioned on the place of residence. Potential students

living in large metropolitan cities had odds of studying almost four times

higher than those from smallest towns and villages. But in the last 20

years, higher education resources moved physically closer to the students,

by the establishment of tertiary institutions in medium-sized cities, by

creating local branches of already existing schools, and by offering a

range of nonstationary programs. This, along with the increasing mobility

of the young, resulted in a drop in the odds ratio between the residents of

the largest cities and smallest settlements from 3.8 in 1995 to 1.5 in 2008.

70. The role of income in determining chances of admission to tertiary

school is also significant, but is much weaker than that of family

educational background. Higher income per household member

increases an individual‟s probability of participating in higher education.

The ratio of odds for the highest and lowest income quintiles was 1.85 in

2008. To assess the magnitude of this ratio, one may refer to the fact that

a difference of similar scale was observed for females as compared to

males (1.76). It seems, therefore, that recently low income is not an

impassable barrier to achieving a higher education level. Interestingly

58

however, a look at the results in particular years during 1994–2008

reveals that the role of money in determining accessibility of tertiary

education had been rising until 2002, and it only began to fall since then.

Today, the impact of income on the probability of participation in tertiary

education is not very different from that observed in the early 1990s.

71. Demographic changes, particularly the population decline in persons

ages 19–24 that began in the mid-2000s, might have helped in

improving the equity of access to higher education in the following

years. As the competition between schools for students becomes tougher,

the skills-based criteria of admission to tuition-free programs are lowered,

allowing more students with a weak family background to pass the

threshold. It seems likely that this effect is responsible for the observed

(since 2003) drop in the impact of income on the probability of

participation in tertiary education. The demographic projections show

that Poland will face a deep (lasting until 2025) decline in the student age

population. Therefore, we may expect a further relaxing of admission

criteria in full-time public programs, as well as decreasing tuition fees in

other modes of studying, although the latter effect is difficult to measure

due to unavailability of data. However it must be stressed that such a

mechanism, although it is likely to improve the equity of access to higher

education, will also depress the quality of it.

72. In the period covered by the research, the mass enrollment in tertiary

schools came at the cost of lowered quality, and quality became an

important aspect of the system’s equity. In the last 18 years, the

proportion of part-time students has increased from 23% to 52%. The

student-staff (teacher) ratio has increased from 7 to 19 and the proportion

of students in the engineering/technical area has fallen from 17% to 7%.

Evidence shows that students from low status families, overrepresented in

part-time programs and nonpublic tertiary schools, are particularly

exposed to the low quality of teaching. Nonpublic schools offer mostly

part-time programs (78% of students). Their student-teacher ratio reached

37 in 2008 compared with 15 for that of public institutions. The share of

the nonpublic sector assessed in Polish research potential is only 1.5%.

Finally, for 95% of their professors, a job in a nonpublic school is a

”second post” employment, while this applies to only 25% of teachers in

public tertiary schools. These and many other statistics presented in the

report show that differences in the quality of tertiary education programs

account for a large part of the inequity issue. The old inequity, based on

lack of access to tertiary programs, transformed into a quality-based

inequity resulting from market segmentation.

59

Policy recommendations

73. The efforts aimed at improving the equity of the Polish higher education

system should be performed within three mutually coordinated channels.

The first relies on shaping students‟ education strategies and raising their

aspirations at the earliest stages of schooling. The second channel should

be focused on providing nondiscriminatory conditions of admission for

students across all social strata. And finally, the Polish higher education

system needs mechanisms enforcing the improvements in quality of

teaching.

74. Intervention at early stages of education should begin with further

improvement in the accessibility of preschools, particularly in rural areas.

Currently the preschool network is developing quickly, but since this

growth is largely financed with E.U. structural funds, it is clearly time to

think of systemic solutions that would allow sustaining this expanded

network once those grants will no longer be available.

75. It was proven that family education background is a significant factor

determining different education patterns, with a particular impact on

sorting between different types of upper secondary schools. Hence,

providing stronger career guidance at the lower secondary level may

substitute as an empowering family environment for students from

underprivileged backgrounds.

76. Providing nondiscriminatory conditions of admission should involve

changes in the social benefits system and student loan scheme. Given the

scarcity of resources, the nonrepayable aid should be focused on

individuals with low income, which would allow increasing the average

amount granted. Financing merit-based benefits from the state budget

should be limited to the few students achieving outstanding results,

providing them with prestigious and (monetary) substantial rewards,

motivating them for further hard work.

77. Modifications are needed in the student loan scheme. Loan eligibility

should not be conditional on age, allowing older individuals to apply.

Also, in the future the maximum income criterion should be abolished, so

that access to loans becomes unrestricted for anyone intending to study at

60

the tertiary level. At the same time, recently implemented changes in the

scheme, offering lowered interest rates and with the state acting as

guarantor for particularly disadvantaged students, should be kept or even

reinforced if the applications of low income individuals are rejected by

the banks. Students should be also given more freedom with respect to

the amount of credit they apply for, as the costs of their studies may vary

significantly depending on the school and type of program they choose.

78. The above-described changes may be considered necessary preparatory

steps to other recommended reforms of the Polish higher education

system, which rely on the introduction of common tuition fees. This

action, politically fragile and widely debated in Poland, would have an

important impact on both equity and quality issues. Currently, financial

burden is unequally imposed on underprivileged social groups and the

principle of nondiscriminatory cost sharing suggests that all students

should participate in the cost of their education. Contrary to common

beliefs, such a solution may lead to more equitable access to education,

given that the proper student support system develops first.

79. Common tuitions fees will affect the quality of teaching, particularly in

nonpublic schools. Their current policy, built upon the “low fees-low

quality” rule, will be changing in response to increased demand from the

students, who now face the necessity of paying a fee regardless of

whether they choose a public or a nonpublic institution. A side effect of

this mechanism may be an increase of the average tuition fee. For this

reason, the proposed changes should be introduced as soon as possible, to

benefit from the demographic decline, which will impose a downward

pressure on the prices demanded by the tertiary schools.

80. It is recommended that the prevalence of part-time study be reduced. The

current share of students enrolled in these studies (52% in 2008) is far

beyond the OECD average (20% for ISCED 5A in 2007), which is not

justified. Hence, the duration of the part-time mode of studies should be

extended (prolonged or intensified) to reach the minimum hours set for a

respective full-time program. Simultaneously, the flexibility of these

programs should be increased (including e-learning opportunities,

61

introducing summer sessions, etc.), in order to enhance adult-learner and

other nontraditional student participation.

81. To increase students‟ interest in scientific and technical faculties, the

upper secondary schools should offer obligatory career-guidance courses,

which would cover the prospects of studying in different faculties and

would discuss current labor market trends. Parents should also be

encouraged to participate in these courses, which would increase the

impact on graduates‟ decisions regarding their field of studies. The

intervention on the supply side, currently being carried out by the

Ministry of Science and Higher Education, should also be continued.

Since 2008, the ministry has identified priority fields and has launched an

open call for projects educating students in these fields. The proposals are

accepted from both public and nonpublic tertiary schools. The winning

institutions receive grants for improving the quality of teaching, and

every student enrolling in the priority field in these schools receives a

special benefit.

82. Strengthening the role of the accreditation agency (Państwowa Komisja

Akredytacyjna) in licensing and assessing tertiary schools is

recommended. More attention should be given to evaluating the

outcomes, highlighting the issue of effectiveness. Criteria based on input

should be tightened, especially regarding the student-teacher ratio, which

currently strongly differs in public and nonpublic TEIs.

83. The development of relevant career guidance and other policy tools

requires improving the data collection system regarding all tiers of

education and beyond them. Currently, maintained databases don‟t allow

tracing students after they graduate from a given tier, and no data (except

for surveys) are systematically collected on the labor market performance

of graduates. Due to poor coordination between state agencies, data on

Poland are missing in many international comparative researches on

education, even in the areas where the information is available. Enhanced

data collection, coordination and distribution would serve a more

effective evaluation of the Polish higher education as a system, but also

as benchmarks for individual schools.

62

84. It must be said that recently we observe some very positive changes in the

policies regarding many of the areas described above. The progress is

visible in the access to preschool education. Some of the recently

introduced changes in the student loan scheme (summarized in the report)

definitely go in the right direction. The government designed package of

reforms aimed at improving the performance of academic research in

Poland was approved by the parliament in April 2010. However, the

analyses presented in this report prove that there is room for even more

improvement in both equity and quality of tertiary schooling in Poland,

and some of the proposed policies may actually serve both these goals.

63

Appendix. Results of the logistic regression of tertiary school accessibility

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Constant .011 .017 .012 .047 .088 .061 .170 .169 .358 .248 .346 .566 .423 .919

Number of siblings .891 .908 .938 .960 .993 .968 .983 .960 .954 .932 .911 .958 .990 .924

Age 1.047 1.028 1.020 .972 .947 .946 .925 .928 .904 .925 .912 .898 .917 .897

Female (male=reference) 1.415 1.458 1.608 1.589 1.675 2.151 2.048 1.954 1.963 2.031 1.944 2.107 1.830 1.761

Living with parents 2.793 2.602 2.142 2.345 1.847 1.653 1.904 1.725 1.892 1.534 1.646 1.701

Father’s education (primary = reference category)

Higher 7.540 7.239 5.973 5.700 6.028 5.132 5.376 3.867 4.161 4.161 3.584 3.602 3.651 3.931

Secondary 2.589 2.619 2.833 2.209 2.186 2.211 2.108 2.114 2.157 2.196 2.357 2.249 2.226 2.046

Basic vocational .487 .590 .559 .511 .526 .515 .598 .544 .572 .544 .591 .527 .583 .553

Quintile of household (1st

quintile = reference category)

2nd

1.556 1.227 1.106 1.445 1.887 1.858 1.663 1.908 1.391 1.620 1.680 1.536 1.615 1.279

3rd

1.699 1.309 1.558 1.539 1.944 1.949 1.985 2.352 2.009 2.142 2.177 1.910 1.775 1.572

4th

1.530 1.249 1.712 2.033 2.637 2.690 2.230 2.662 2.448 2.641 2.366 2.308 1.967 1.712

5th

1.634 1.418 1.767 1.880 2.742 3.172 3.000 3.350 2.584 2.592 2.819 2.919 2.471 1.852

City population (rural = reference category)

More than 500,000 3.849 4.211 2.110 1.905 1.596 1.495 1.699 1.604 1.392 1.462 1.882 1.818 1.443 1.488

200,000-500,000 3.414 4.319 2.584 1.930 2.034 2.201 1.889 1.976 1.582 1.723 1.719 1.515 1.766 1.542

100,000-200,000 1.885 2.362 1.610 1.369 1.449 1.615 1.167 1.390 1.272 1.228 1.267 1.377 1.047 1.309

20,000-100,000 1.611 1.493 1.622 1.446 1.400 1.463 1.140 1.184 1.342 1.152 1.241 1.116 1.170 1.237

Up to 20,000 1.088 1.316 1.130 1.231 1.191 1.393 1.217 1.374 1.413 1.332 1.321 1.015 1.183 1.220

Table 14 (A1). Estimation of the logistic model of participation in tertiary education. Values of the odds (eB ).

Source: Author‟s calculations based on Household Budget Survey data 1995–2008.

64

List of Tables

Table 1. Enrollment in Polish tertiary education, academic year 2008/09 ............................................. 5

Table 2. Students benefitting from state support system, academic year 2008/2009 .............................. 9

Table 3. Student performance on the science scale by elements of the PISA index of economic, social

and cultural status (ESCS), 2006 ........................................................................................................... 17

Table 4. Students‟ fathers with particular educational attainment or occupational status, ratio to

corresponding proportion in general population (males ages 40–60 years), 2005–2007 ...................... 39

Table 5. Characteristics of Polish higher education system in 1990 and 2008. .................................... 44

Table 6. Labor market status of 1998–2005 graduates from different levels of education ................... 48

Table 7. Labor market status of graduates by type of TEI and mode of study, 2008. ........................... 49

Table 8. Skills and abilities obtained by public and nonpublic students through tertiary education, as

perceived by TEI chancellors (2004). ................................................................................................... 50

Table 9. Final-year students‟ opinions on skills and abilities gained during their tertiary education –

Wielkopolskie voivodeship. .................................................................................................................. 51

Table 10. Labor market status of tertiary graduates – by type of TEI and mode of study (1998–2005

graduates). ............................................................................................................................................. 52

Table 11. Employment quality for 1998–2005 master‟s and bachelor‟s graduates............................... 53

Table 12. Expected and actual salaries for 1998–2005 master‟s and bachelor‟s graduates .................. 54

Table 13. Labor market outcomes by occupational groups (1998–2005 graduates). ............................ 55

Table 14 (A1). Estimation of the logistic model of participation in tertiary education. Values of the

odds (eB ). .............................................................................................................................................. 63

65

List of Figures

Figure 1. Annual expenditure per tertiary student by type of service, 2006 ........................................... 7

Figure 2. Total public and private expenditure on tertiary education, 2006. ........................................... 8

Figure 3. Annual average growth of tertiary education for ages 25–64 (1998–2006) .......................... 12

Figure 4. Secondary graduation (ISCED 3A) rates and tertiary entry rates (ISCED 5A), 2007 ........... 14

Figure 5. Variance in student performance between schools, on the science scale, 2006 ..................... 16

Figure 6. Participation in general secondary education of persons ages 16–18 years by quintiles of

household revenues per capita (1994–2008) ......................................................................................... 17

Figure 7. Participation in general secondary education for persons ages 16–18 years, by father‟s

education level (1994–2008) ................................................................................................................. 20

Figure 8. Persons ages 19–26 years attending any tertiary program (1994–2008) ............................... 23

Figure 9. Participation in tertiary education for persons ages 19–26 years, by quintiles of household

revenues per capita (1994–2008)........................................................................................................... 24

Figure 10. Participation in tertiary education for persons ages 19–26 years, by person‟s place of

residence (1994–2008) .......................................................................................................................... 25

Figure 11. Participation in tertiary education for persons ages 19–26 years, by father‟s education level

(1994–2008) .......................................................................................................................................... 27

Figure 12. Participation in tertiary education for persons ages 19–26 years, by father‟s occupation

(1997–2008) .......................................................................................................................................... 28

Figure 13. Students ages 19–26 years, in part-time programs, by income quintile (1997–2008) ......... 29

Figure 14. Students ages 19–26 years in part-time programs, by father‟s education (1997–2008) ...... 30

Figure 15. Students ages 19–26 years in part-time programs, by person‟s place of residence (1997–

2008) ...................................................................................................................................................... 31

Figure 16. Students ages 19–26 years in nonpublic tertiary schools, by quintiles of household revenues

per capita (2001–2008) .......................................................................................................................... 32

Figure 17. Students ages 19–26 years in nonpublic tertiary schools, by father‟s education (2001–2008)

............................................................................................................................................................... 32

Figure 18. Students ages 19–26 years attending paid tertiary programs, by quintiles of household

revenues per capita (2001–2008)........................................................................................................... 34

Figure 19. Students ages 19–26 years attending paid tertiary programs, by father‟s education level

(2001–2008) .......................................................................................................................................... 34

Figure 20. Odds ratios for access to higher education, by father‟s educational attainment. ................. 36

Figure 21. Odds ratios for access to higher education, by income level. .............................................. 37

Figure 22. Odds ratios for access to higher education, by place of residence. ...................................... 38

Figure 23. Current and projected population for the ages 19–24 years cohort in Poland. ................... 41

66

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