Teachers’ labor market, PISA and wages
Karmen Trasberg, Viktor Trasberg,
University of Tartu, Estonia
Introduction
The paper is motivated by the latest PISA outcomes (2012), which was focused on math skills
among lower secondary school students. Our particular interest is related with Estonian
students’ position among the studied countries. Estonia performed surprisingly well as the
country was placed 11th in the world among 65 countries and economies under the
surveillance. The country’s position also has improved since the last survey.
Programme for International Student Assessment (PISA) survey reveals different aspects of
quality of education. That is a very broad issue, which depends on wide range of components.
Generally, different micro level factors can be identified, which are related with particular
system of education or school’s management aspects. There are many studies, analyzing how
students' gender, ethnicity, socio-economic background or the school's governance model can
impact the achievements (Bieber, Martens, 2011; Elmeroth, 2012; Easton, 2013, etc).
The attractiveness of the teaching profession depends on many factors, including the
distribution of earnings during the career path. Good starting salary is considered to be one of
the predisposing measures, which allows hire highly professional teachers. Teachers pay in
world's top educational systems (in the OECD countries) is higher than average or more than
average wage across the wage earners. There are studies, demonstrating how teacher salaries
(particularly performance pay) correlate with students' achievements (Woessmannn, L. 2011;
Akiba, M.; Chiu Y.L.; Shimizu, K.; Liang, G, 2012). Intuitively, higher per capita GDP level
means also higher teacher salaries. And higher salaries can help school systems to attract the
best candidates to the teaching profession, and give signals that teachers are regarded and
treated as professionals (OECD, 2013).
In the OECD countries, teachers' salary is the largest component in the total educational
expenditure (approximately 80%). As mentioned, teachers’ salary level has a clear impact on
educational system quality and teachers motivation.
This paper will open up a new avenue for PISA studies. That is – we explore how the
proximity of population living environment or certain life-mode has impact on PISA results.
Considering the PISA 2012 results, we are witnessing some very striking differences between
the best and worst performers. In the highest positions are the countries which are extremely
urbanized or have a very high population density (Table 1). In the PISA ranking, in the top 6
are 5 countries with urbanization rate of 100 percent1. The opposite end countries are
characterized as countries with significantly lower urbanization rates and with considerably
smaller population density levels.
Table 1. PISA ranking and population allocation
PISA math
rank number
1
2
3
4
5
6
7
8
9
10
11
……
59
60
61
62
63
64
65
67
68
69
Shanghai-China
Singapore
Hong Kong-China
Chinese Taipei
South-Korea
Macao-China
Japan
Liechtenstein
Switzerland
Netherlands
Estonia
613
573
561
560
554
538
536
535
531
523
521
100
100
100
100
83
100
79
22
67
90
79
Population
density,
persons per
km2
3,700
7,669
6,516
646
505
20,069
337
230
196
404
30
Costa Rica
Albania
Brazil
Argentina
Tunisia
Jordan
Colombia
Qatar
Indonesia
Peru
407
394
391
388
388
386
376
376
375
368
60
43
82
88
6
79
75
93
42
73
91
98
23
14
66
74
42
175
124
23
CountryTerritory
PISA math
result
Urbanization
rate
Source: PISA 2012 Report; CIA World Factbook.
This leads to the question – how does the population living environment particularities
(similarities or dissimilarities) influence the PISA results?
Therefore, a keyword for our study is homogeneity. In other words – homogeneous living
environment is positively correlated with PISA results. Intuitively, if the population density or
1
Interesting case is Liechtenstein. Country has rather limited territory and size of population. Most of
population is residing areas, which is considered as rural. However, the population density in Liechtenstein is
very high.
2
compactness is high, the PISA results are more homogeneous. Our working hypothesis is that
successful countries are those where students live broadly in comparable (homogenous)
conditions. Additionally, the high density should be considered in combination with urban
environment. Therefore, those two factors – urbanization and territorial compactness of
societies is supporting PISA performance.
Compactness, which is measured by the population density and urbanization rate, puts
hypothetically schools (and student body) into more similar conditions. Variance of schools
standards, curricula and teaching quality is smaller, than in the large territories with sparsely
located population. More homogeneous living mode (e.g. urban), in turn, makes school
systems more heterogeneous. In opposite, if countries have different regions with different
life modes (e.g urban and rural) and those regions are spread widely over country’s territory –
also the school differences will increase. The schools become then more diverse and as an
outcome, the schools’ quality and educational standards became more heterogeneous. Such a
heterogeneity transfers into higher PISA results variance and differentiation.
Considering that, the paper brings out some correlations between PISA results and countries
homogeneity factors – like urbanization, population density and income inequality. Separately
will be characterized teachers’ salaries impact on PISA results. Estonian PISA performance
factors are also analyzed in those frames.
What is PISA?
The Programme for International Student Assessment (PISA) is an international survey,
organized by the OECD which aims to evaluate education systems worldwide by testing the
skills and knowledge of 15-year-old pupils. To date, students representing more than 70
economies have participated in the assessment. Since 2000, every three years, fifteen-year-old
students from randomly selected schools worldwide take tests in the key subjects: reading,
mathematics and science, with a focus on one subject in each year of assessment. In PISA
2000 and 2009 reading literacy was the main domain, PISA 2003 and 2012 focused on
mathematics and PISA 2006 had science as the major domain.
PISA offers insights for education policy and practice, and that helps monitor trends
in students’ acquisition of knowledge and skills across countries and in different demographic
subgroups within each country. Results reveal by showing what students in the highestperforming and most rapidly improving education systems can do. The findings allow policy3
makers around the world to gauge the knowledge and skills of students in their own countries
in comparison with those in other countries, set policy targets against measurable goals
achieved by other education system, and learn from policies and practices applied elsewhere.
Estonian results in PISA
Estonia has participated in PISA 2006, 2009 and 2012 cycles. In the last cycle - 206 Estonian
schools participated, 166 schools were with Estonian as the language of instruction, 37 with
Russian as language of instruction and 3 mixed language schools. 79% of the students (about
3800) took the test in Estonian and 21% of the students (995) took it in Russian. As the total
number of 15 year-olds in Estonia in 2012 was 12 439, almost half of the PISA age cohort
was assessed.
The results showed that the performance of Estonian 15-year old students ranks among the
top achievers. In European comparison, Estonia share 3.-6. place with the Netherlands,
Finland and Poland. Among all participating countries Estonia ranks 10 - 14. Also the
students attitudes toward learning are favourable - 81% of Estonian students think that
mathematics is important and they will need it in their future studies. 76% of Estonian
students are satisfied with their school, 66% of the Estonian students say they feel happy at
school. As a rule, the happiness factor and student performance are linked weakly in most
countries - the happiest students are in countries where the performance is lower.
There are several reasons behind good results and the following could be highlighted:
(a) Estonian student performance is not influenced by student socio-economic
background.
Performance in mathematics is not affected by student socio-economic background - more
than a third of students with low socio-economic background are among the best performers.
It is caused by implementing principles of comprehensive school, broad national curriculum
for all students and tutoring of low achievers, relatively small classroom size, subsidized
school meal in lower secondary school etc.
(b) Lowest amount of low proficiency students in Europe.
Estonia is among the top countries that have the smallest amount of students that have not
reached the baseline - second proficiency level. Students at this level should be able to
manage in everyday life. Most of Estonian students have reached this level in all three
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assessment domains (in reading 90,9% of students, in maths 89,5% of students, in science
95%). The trend during the six years has been positive (Summary of PISA 2012 results for
Estonia).
At the same time PISA results draws attention to problem areas, which need attention and
further carefully considered actions - there is a performance gap between urban ( big
towns 534 p, small towns 518 p) and rural schools (509 p). The big variability of student
performance rise the question - why schools in Tallinn, Tartu or on the islands are the best
performers, but counties show more modest results. There are also big regional differences –
in math the performance gap between Hiiu county (the best performing in Estonia) and IdaViru county is 62 points, which corresponds to one and half school year. The performance of
students from Russian medium schools is significantly lower than for students in Estonian
medium schools. In comparison to PISA 2006 and 2009 the performance of Russian students
has improved, however, but the gap is still big – 36 score points which can be considered as
close to one year of schooling. This means that students, graduating Russian medium
comprehensive schools are significantly less prepared to make choices concerning further
studies and participation in lifelong learning.
Teachers pay and PISA
As it was demonstrated, teachers’ salary is highly correlated with PISA results. In turn,
teachers’ salary depends on country’s GDP level.
An OECD thematic report “Does performance-based pay improve teaching?” used evidence
from PISA to identify whether recognizing and rewarding teaching performance through pay
raises student attainment. About half of the OECD countries participating in PISA reward
teacher performance financially. Some high performing education systems have used
performance-based pay while others have not. Examination of the overall picture revealed no
relationship between average student performance in a country and the use of performancebased pay schemes for teachers.
The picture changed when taking into account how well teachers had been paid overall in
comparison with national income. The report suggested that even if performance based pay is
a viable policy option, it is important to know how to implement the system effectively
(OECD, 2012).
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Salaries are just one of many factors that motivate teachers, but they are a key consideration
in attracting the best candidates and retaining the best teachers. Low salaries are likely to
damage morale and can lead teachers to switch to other careers. At the same time, teacher
salaries make up the largest share of most education budgets, so they need to be set at a
realistic level to ensure that enough teachers can be recruited. The level of teacher salaries
influences education quality (UNESCO, 2014).
Salaries in terms of the purchasing power of the lower side of the transition countries are
followed (like in Estonia, Hungary, Czech Republic), where the starting salary as a well as
service-length related salary wage growth were relatively small. However, should be noted
that this is only a salary level; it will not take into account other bonuses for novice teachers
to be supported (e.g. a young teacher start-up money, etc..).
The study also analyzed the relationship between teachers' salaries and student achievements.
PISA results show that in the number of countries educational investments correlate with the
academic performance (Finland, Japan, Korea). However, the certain countries, where
education spending is relatively low, achieved excellent results (including Estonia). And the
converse is also true - Austria, France, Germany having relatively high educational
expenditure, but the results are average or below of the OECD indicators.
It turned out that the good results and earnings are positively related in Asian countries, as
well as the Netherlands. In the Nordic countries, high level of wages is linked with high
performance in Finland. Estonia excels in high PISA results, but the teachers' salaries are in
the lowest pay segment. Consequently, we expect that Estonia's current teacher’s effective
work is related with some other motives - whether it be a teacher social status, confidence,
mental and physical working environment, the long summer holidays, social security, etc.
However, the teaching profession at the current wage conditions will not be attractive and
motivating for the younger generation in the long term.
Homogeneity and PISA
How PISA results correlate with homogenity factors? In following are studied European
countries (33 countries) PISA results and correlation with various indicators (Table 2)
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Table 2. Correlations between PISA and homogeneity indicators
Pop
PISA
GDP
Gini
Density2
math result per capita
coefficient Person per
2012 EUR, 2012
km2
PISA math result 2012
1
GDP per capita
EUR, 2012
0.500**
Gini coefficient
0.472**
Population density2,
person per km2
0.342*
Urbanization rate
0.394*
Teachers’ minimal
salary, EUR, 20123
0.433**
0.500**
1
-0.493**
-0.472**
Urbanization
rate2
0.342*
-0.493**
1
-0.129
Teachers
min
salary
EUR,
20123
0.394*
0.433**
0.572**
0.951**
-0.315*
-0.345*
0.367*
1
0.572**
-0.315*
0.951**
-0.345*
1
0.367*
0.561**
0.561**
1
Source: Eurostat1 and World Bank2 database; Eurydice3 (lower secondary student teachers)
** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level
There are chosen set of indicators, which were named earlier. Urbanization rate, population
density and Gini coefficient has chosen as homogenity indicators. GDP level per capita and
teachers salary level are additional components in explaining PISA results. Intuitively, they
should be closely related with each other and also with urbanization rate. In the table are
presented only those correlation levels, which are statistically significant. As the table
presents, PISA results correlate significantly with all presented indicators.
What the correlations tell us?
Countries GDP per capita level is strongly and positively correlated with PISA. Similarly, it
is highly correlated with teachers’ salary. Those are rather expected outcomes. Similarly,
level of teacher’s salary is positively related with PISA results, urbanization and population
density. One can say that in the urban schools have higher teacher salaries, which in turn,
have a positive effect on PISA results. Separately will be teachers’ pay systems and level
issues explained below.
High Gini coefficient level indicates widespread income differences in society. However, the
coefficient level is not very clearly related with country’s GDP level. For example, Finland
and Belarus have about same level of income inequality. However, their income levels differ
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manifold. Therefore, it is intuitively difficult to predict the strength and sigh of relationship
between income inequality and PISA results. Although, as the correlation level demonstrates,
the Gini coefficient is strongly, but negatively related with the PISA outcomes. That is –
higher income inequality level means also lower PISA result! Heterogeneity of society’s
incomes affects PISA result negatively. Also, a society with high income differences is lower
teacher salary level.
The table presents that population density and urbanization level is positively correlated with
PISA results. That outcome is in accordance with our hypothesis, that homogeneity in living
mode has positive impact on higher PISA results.
To conclude this sub-chapter, it is possible to find statistically significant correlations
between homogeneity characteristics and PISA outcomes. Nevertheless, correlative links do
not proof causality relationships between the variables. In the next, an econometric model is
generated to expose homogeneity factors impact on PISA results.
Does homogeneity factors have impact on PISA outcomes?
There was constructed linear OLS simple regression model to test earlier named factors
impact on PISA score.
PISA score = b0 + b1 factor + ε
Dependent variable: PISA score in points
Predictors (factors): GDP per capita ; Gini coefficient ; Population density; Urbanization
rate
Individual tests confirm statistically significant impact on PISA score. Therefore, the
regressions’ outcomes go hand-in-hand with earlier correlation results. Definitely, all those
variables explain only a part of score fluctuations.
In following was tested the same factors in combination to explain the PISA score variations.
A multiple regression model was constructed in a following way:
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PISA result = b0 + b1 GDP per capita + b2 Gini + b3 Pop.Density + b4 Urbanization + ε
se
(510.06)
(0.00)
(1.179)
(0.047)
(0.477)
t
10.13
0.692
-1.809
1.204
0.801
0.495
0.082
0.024
0.043
Sig.
R2 = 0.338
0.000
Adjusted R2 = 0.240
dfregression = 4
dftotal = 31 F= 3.448
Sig.= 0.021
As the test results indicate, those factors as provided such a combination have an impact on
PISA results. The model F-test indicates statistical significance. However, those variables
explain only 24% of variation of PISA outcomes. Despite the general significance of the
model, the variables individually do not proof their significant impact on PISA results.
Therefore, in the current combinations those variables proof positive impact of homogenity
factors to the PISA outcomes.
Conclusions
There are various factors, which have clear impact on PISA results - like nation income level,
teachers’ motivation aspects or school's governance model. In this paper we discussed some
other factors - like urbanization, population density and income inequality which might have
impact on the PISA score. Those are named as homogeneity factors. We argue that high
urbanization rate and population density equalizes school standards and therefore, increases
student achievement. Otherwise, countries with extensive territories and different life modes
have lower PISA outcomes due to high variance in school quality. Correlation analyses,
which included a set of European countries, supported that an argument. However, regression
analyses do not show strong causal relationship between homogeneity factors and PISA score.
The study also analyzed the relationship between teachers' salaries and student achievements.
PISA results show that in the number of countries educational investments correlate with the
academic performance. And there are countries with relatively high educational expenditure,
but the results are average or below of the OECD indicators. It turned out that the good results
and earnings are positively related in Asian countries, as well as the Netherlands. In the
Nordic countries, high level of wages is linked with high performance in Finland. Estonia
excels in high PISA results, but the teachers' salaries are in the lowest pay segment.
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