Proceedings of the 12th European Conference on Knowledge ...

Camelia Burja and Vasile Burja
knowledge endowments and per capita GDP growth in <strong>European</strong> regions. Cohen and Soto (2001)
also asserted <strong>the</strong> role <strong>of</strong> human capital for <strong>the</strong> economic growth and suggested that <strong>the</strong> differences <strong>of</strong>
opinions on human capital’s role derived from <strong>the</strong> quality <strong>of</strong> data used in analysis.
In <strong>the</strong>se context, in order to study <strong>the</strong> link between <strong>the</strong> performance <strong>of</strong> KE and <strong>the</strong> educational
system, we used as input variables <strong>the</strong> indicators that show <strong>the</strong> size <strong>of</strong> <strong>the</strong> labour force trained for <strong>the</strong>
active integration on <strong>the</strong> labour market in <strong>the</strong> areas that ensure social progress, and appropriate
investment efforts: students (ISCED 1_6) aged 15-24 years - as % <strong>of</strong> corresponding age population
(capacity <strong>of</strong> economy to absorb, develop and diffuse Knowledge), tertiary graduates in science and
technology (innovative potential <strong>of</strong> economy) and public expenditure on education.
Although <strong>the</strong> evaluation indicators <strong>of</strong> <strong>the</strong> educational system are more numerous, <strong>the</strong> DEA
methodology has imposed a restriction on <strong>the</strong>ir number. For <strong>the</strong> results to be relevant, it’s necessary
that <strong>the</strong> total number <strong>of</strong> input and output variables used in <strong>the</strong> analysis should be lower than <strong>the</strong>
number <strong>of</strong> studied entities (Cooper et al. 2006).
The lack <strong>of</strong> data for a few indicators in Malta led to confining <strong>the</strong> analysis to a group <strong>of</strong> 11 countries
that joined <strong>the</strong> EU recently. The EU statistics are mostly updated until 2008, and 2004 marked <strong>the</strong>
completion <strong>of</strong> negotiations for Romania’s accession to <strong>the</strong> EU, which is why <strong>the</strong> progresses recorded
since <strong>the</strong>n in terms <strong>of</strong> Knowledge economy are interesting.
In Romania, a part <strong>of</strong> <strong>the</strong> indicators that characterize education and training have recorded important
progresses during <strong>the</strong> analyzed period. There are still gaps in comparison with most countries that
recently joined <strong>the</strong> EU in terms <strong>of</strong> Students aged 15-24 years and Public expenditure on education.
The gross increase <strong>of</strong> domestic product per capita is also important, but GDP remained low in 2008
compared with o<strong>the</strong>r countries, except Bulgaria (table 1).
Table 1: Education and training, 2004-2008
Ţări GDP at market prices;
PPS per inh.
Euro
Students (ISCED 1_6)
aged 15-24 years –
as % <strong>of</strong> corresponding
age population
Tertiary graduates
in science
and technology
per 1 000 <strong>of</strong>
population aged
20-29 years
Public expenditure
on education
% <strong>of</strong> GDP
2004 2008 2004 2008 2004 2008 2004 2007
Bulgaria 7500 10900 48,9 51,9 8,5 9,1 4,51 4,13
Czech Republic 16300 20200 59,2 61,6 7,4 15,0 4,37 4,20
Estonia 12400 17000 63 60,8 8,9 11,4 4,92 4,85
Cyprus 19600 24400 42,3 43,6 4,2 4,0 6,70 6,93
Latvia 9900 14100 64,8 61,0 9,4 8,8 5,07 5,0
Lithuania 10900 15300 69,1 69,0 17,5 17,8 5,19 4,67
Hungary 13700 16200 59,7 64,5 5,1 6,1 5,41 5,20
Poland 11000 14100 68,6 70,3 9,4 14,1 5,41 4,91
Romania 7400 11700 47,4 56,9 9,8 15,2 3,28 4,25
Slovenia 18700 22800 67,6 71,0 9,3 10,7 5,76 5,19
Slovakia 12300 18100 52,1 56,5 9,2 15,0 4,20 3,62
Source: Eurostat database
* 2005 **Students at ISCED levels 5-6 enrolled in <strong>the</strong> following fields: science ma<strong>the</strong>matics
computing engineering manufacturing construction - as % <strong>of</strong> all students
Applying <strong>the</strong> DEA model in dynamic, as constant return to scale (CRS) and output-orientated, leads to
a distribution <strong>of</strong> <strong>the</strong> countries in <strong>the</strong> analyzed group in accordance with <strong>the</strong> performance level <strong>of</strong> <strong>the</strong>
educational system (table 2).
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