RURAL DEVELOPMENT
Final Report
Country:
Slovenia, Statistical Office of the Republic of Slovenia
Rural Development Final Report
Statistical Office of the Republic of Slovenia
1. INTRODUCTION
The final report presents work on and the results of the Rural Development Project carried out
by the Statistical Office of the Republic of Slovenia (SORS).
First the organization of the work and approaches used are presented. That is followed by a
detailed description of the results of the Rural Development indicators for each subject
separately. The description of the indicators includes information about sources of the data,
broad classification of data quality and information about metadata and definitions. In case of
low data quality possibilities of improvement are discussed with the emphasis on problematic
issues of providing data on expected territorial levels.
In the end the statistics on reported data in the Rural Development Project are introduced.
2. BACKGROUND
The Rural Development Project is based on collecting comparable statistics for rural areas,
which include collection of the data (indicators) for the purposes of territorial rural
development policy. In this stage of the project a set of indicators was developed to cover the
fields of demography and migration, economy and human capital, accessibility to service &
infrastructure and social well-being. The set of variables for the Rural Development data
included 25 indicators.
Data were collected at two levels of spatial units, following two different spatial concepts,
OECD definition and Eurostat’s “Degree of Urbanisation’’, both based essentially on the
population density.
The first mentioned, OECD definition, distinguishes two hierarchical levels – local
(commune=LAU 2) and regional (district=NUTS 3). At the local level rural communities are
defined as those that have population density below 150 inhabitants/km2. At regional level
territorial units are distinguished by their degree of rurality, depending on the share of the
region’s population living in rural communities. There are three types of regions used:



predominantly rural regions: >50 % of the population living in rural communities.
significantly rural regions: 15-50 % of the population living in rural communities.
predominantly urban regions: <15 % of the population living in rural communities.
According to the population density and share of people living in rural communities, each
NUTS 3 region of the EU is put in one of the three types.
Classification of statistical regions in Slovenia (NUTS 3) according to the OECD concept is
shown in Figure 1 below. Only 4 of the 12 regions are classified as significantly rural and 8 as
predominantly rural. None of statistical regions is classified as predominantly urban.
Significantly rural regions are Zasavska, Gorenjska, Obalno-kraška and Osrednjeslovenska,
while other regions (Pomurska, Podravska, Koroška, Savinjska, Spodnjeposavska,
Notranjsko-kraška, Goriška and Jugovzhodna Slovenija) are predominantly rural.
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Figure 1: Urban/rural classification of statistical regions (NUTS 3) in Slovenia according to
the OECD concept
The other concept is currently used by EUROSTAT within some statistical surveys (Labour
Force Survey, Survey on Income and Living Conditions). It is based on:
 population density criteria,
 territorial agglomeration of local units (municipalities) that suit the population density
criteria,
 minimal number of population.
The degree of urbanisation distinguishes three types of areas:

densely populated area which is a contiguous set of local areas, each of which has a
density > 500 I/km2, and where the total population for the set is at least 50.000
inhabitants;

intermediate area, a contiguous set of local areas, not belonging to the densely
populated area, each of which has a density > 100 I/km2, and either with a total
population for the set of at least 50.000 inhabitants or adjacent to a densely populated
area;

thinly populated area which is a contiguous set of local areas, neither belonging to a
densely populated area nor to an intermediate area.
A set of local areas (communes or municipalities) totalling less than 100 km2, not reaching the
required density, but entirely enclosed within a densely populated or intermediate area, is to
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be considered to form part of that area. If it is enclosed within a densely populated area and an
intermediate area it is considered to form part of the intermediate area.
According to the described Eurostat’s concept the majority of Slovenian municipalities (71%)
is classified as thinly populated, while only two municipalities (1%) are classified as densely
populated (Ljubljana and Maribor). The other 54 municipalities represent intermediate area.
Figure 2: Urban/rural classification of Slovenia according to the Degree of urbanisation
Data in the Rural Development project were collected parallel using both approaches. The
reference period for collecting data were years from 2000 to 2004.
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3. ORGANISATIONAL SET-UP
The Rural Development Project was carried out by the Statistical Office of the Republic of
Slovenia (SORS) with the Regional Statistics Department in the coordination role. On one
hand coordination was carried out with Eurostat. Since we had to confront different problems
considering methodological ambiguities during our work, we addressed topical questions to
Eurostat’s contact person Mr. Goll to avoid possible misunderstandings.
On the other hand work on collecting rural statistics included also other statistical
departments of SORS - Demography Statistics, Labour Statistics, Level of Living Statistics,
Social Services Statistics, National Accounts Statistics, Agriculture, Forestry, Fishing and
Hunting Statistics, and Information Society and Tourism Statistics.
The Regional Statistics Department prepared for all subject domains an exhaustive
description of the running project with precise instructions about data that had to be collected
and in which manner. Beside methodology and definitions that had to be followed the code
list of classified territorial units (LAU 2) for aggregating data according to the Degree of
Urbanisation concept was provided. The Regional Statistics Department previously also
prepared data tables which were filled by subject-matter departments. In most cases
departments had sent already aggregated data, but there were also situations were aggregation
was done by the Regional Statistics Department. Besides collecting absolute figures,
calculations of all Rural Development indicators were carried out. An exception was the
Proximity to school indicator, which is planned to be done by Eurostat.
For the data based on the Labour Force Survey a complete calculation for NUTS 3 level had
to be done. The existing data are namely mainly calculated and published only at NUTS 2
level, which at the moment represents the whole territory of Slovenia. Calculations of the data
and estimates were done by the Sampling and Survey Methodology Department.
Another task besides providing data for all included subject-matter departments was also to
define the source of data and estimate their quality. A more detailed analysis of the data
quality is described in the continuation of this report. The analysis in the last section also
shows coverage with required Rural Development Project data from the territorial aspect and
problems with providing data. The table below shows data that were provided in the Rural
Development Project.
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Table 1: Availability of data collected for indicators in the Rural Development Project
Issue
I. Demography – migration
Indicator
1. Population change
2. Net-migration
3. Natural population change
4. Age structure changes
II. Economy –Human capital
1. Forms of employment
2. Importance of different sectors
3. Importance of public sector
4. Capacity of collective tourist
accommodation
5. Occupancy of collective tourist
accommodation
6. Weight of manufacturing
7. Weight of tertiary sector
8. Relative changes of
unemployment
9. Human capital
10. Potentially available human
resources
11. Relative changes of
employment
1.Structure of agricultural
IIa. Economic structure &
Performance (agriculture and forestry) businesses
2. Weight of primary or
agricultural sector
3. Productivity of the agricultural
holdings
III. Accessibility to services –
infrastructure
1. Availability of roads/rails
2. Supply with schools
3. Proximity to primary schools
IV. Social well-being
1. Households disposable income
Availability
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Data will be
provided by
Estat
+
+
+
2. GDP per capita
+
3. At-risk-poverty rate
+
4. Quality of life
-
5. Quality of life
Comment: (+) = available, (-) = unavailable
+
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4. ACCURACY OF DATA
The quality of the Rural Development data in general depends on the source that the data are
based on. In the case of administrative sources the quality of data is mostly good while the
quality of survey data varies from good to low. The quality of data that are based on the
Labour Force Survey (LFS) and the Farm Structure Survey (FSS) is categorised with the
coefficient of variation, while the quality of other survey data is marked according to the
extent of the coverage of observation units in the statistical surveys.
As said, the quality of data in administrative sources (the Central Population Register, the
Register of Spatial Units) and statistical register data (the Statistical Register of Employment)
is good. The same is true for the census data (the 2000 Agriculture Census, the 2002
Population Census).
The quality of other statistical data based on the statistical surveys which collect data using
different questionnaires (tourism data, number of schools) are considered as full coverage of
the observation units in the statistical surveys. The data quality is categorised as good. The
same is considered for Regional Accounts data – the Regional Gross Domestic Product
(GDP) and the Regional Gross Value Added (GVA) for manufacturing and the tertiary sector.
In the case of the regional GVA in agriculture and forestry data are estimated to be of medium
quality and are shown for the whole NACE A section. It is considered that working data used
for the calculation of forestry are of low quality.
By calculating the Labour Force Survey estimates, the following internal criteria for accuracy
were taken into consideration:




Estimates with the coefficient of variation CV =< 10% are published without
limitation and are considered as high quality (model: 10,500 persons or more).
Estimates with 10% < CV = < 20% are considered as data of medium 1 quality and are
stated in single parentheses (model: 3,500 to 10,499 persons).
Estimates with 20% < CV= < 30% are considered as data of medium 2 quality and are
stated in double parentheses (model: 1,000 to 3,499 persons).
Estimates with accuracy less that CV > 30% are considered as low quality data and are
not published (model: fewer than 1,000 persons).
The categorisation of data quality based on the Farm Structure Survey differs from the LFS
categorisation because of stricter precision requirements in agricultural statistics. Namely, the
collection of data on agriculture at the level of EU is regulated by several regulations and
decisions. Each legal act defines also precision requirements, e.g. decisions on livestock
statistics request CVs of 2% for pigs total, 2% for sheep and goats, 1% for cattle total;
Regulations on crops request CVs of 1% for cereals total, 3% for dried pulses and some other
groups of crops.
The quality of the FSS data is categorized as followed:
- Estimates with the coefficient of variation CV = 5<10% are considered as data of medium
quality.
- Estimates with CV =>10% are considered as data of low quality and are unreliable data.
The decision for classes of CVs up to 5%, 5–10%, etc., was taken by Eurostat and due to
requests in legislation national principles in the field of agriculture can not be applied.
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5. TERRITORIAL BREAKDOWN 2000-2004
Since the year 2000 the Republic of Slovenia has been using the Standard Classification of
Territorial Units (SKTE) as the obligatory national standard to be used in making records,
collection, processing, analysing, dissemination and presentation of data on the territorial
division of the Republic of Slovenia. According to the Decree on the SKTE, the territory of
Slovenia is broken down into 11 levels. The first two levels, SKTE 0 and SKTE 1, cover
Slovenia as a whole, while SKTE 2 level has two units. SKTE 3 covers the breakdown into
statistical regions (12), SKTE 4 covers the breakdown into administrative units (58) and
SKTE 5 covers the breakdown into municipalities (193).
According to Eurostat’s classification on territorial units for statistics (NUTS) in the period of
2000-2004, the territory of Slovenia was broken down into 3 levels. At NUTS 1 and NUTS 2
Slovenia was one unit and at NUTS 3 Slovenia was broken down into 12 units – statistical
regions (SKTE 3).
Table 2: Individual statistical regions with municipalities included
NUTS 3
Pomurska
Area
1337 km2
Municipality
2170 km2
Benedikt, Cerkvenjak, Destrnik, Dornava, Duplek,
Gorišnica, Hajdina, Hoče - Slivnica, Juršinci, Kidričevo,
Kungota, Lenart, Lovrenc na Pohorju, Majšperk, Maribor,
Markovci, Miklavž na Dravskem polju, Oplotnica,
Ormož, Pesnica, Podlehnik, Ptuj, Rače - Fram, Ruše,
Selnica ob Dravi, Slovenska Bistrica, Starše, Sveta Ana,
Sveti Andraž v Slov. goricah, Šentilj, Trnovska vas,
Videm, Zavrč, Žetale
1041 km2
Črna na Koroškem, Dravograd, Mežica, Mislinja, Muta,
Podvelka, Prevalje, Radlje ob Dravi, Ravne na Koroškem,
Ribnica na Pohorju, Slovenj Gradec, Vuzenica
2384 km2
Bistrica ob Sotli, Braslovče, Celje, Dobje, Dobrna, Gornji
Grad, Kozje, Laško, Ljubno, Luče, Mozirje, Nazarje,
Podčetrtek, Polzela, Prebold, Radeče, Rogaška Slatina,
Rogatec, Slovenske Konjice, Solčava, Šentjur*, Šmarje
pri Jelšah, Šmartno ob Paki, Šoštanj, Štore, Tabor,
Velenje, Vitanje, Vojnik, Vransko, Zreče, Žalec
264 km2
Hrastnik, Trbovlje, Zagorje ob Savi
885 km2
Brežice, Krško, Sevnica
2137 km2
Bled, Bohinj, Cerklje na Gorenjskem, Gorenja vas Poljane, Jesenice, Jezersko, Kranj, Kranjska Gora, Naklo,
Preddvor, Radovljica, Šenčur, Škofja Loka, Tržič,
Železniki, Žiri, Žirovnica
SI001
Podravska
SI002
Koroška
SI003
Savinjska
SI004
Zasavska
Beltinci, Cankova, Črenšovci, Dobrovnik/Dobronak,
Gornja Radgona, Gornji Petrovci, Grad, Hodoš/Hodos,
Kobilje, Križevci, Kuzma, Lendava/Lendva, Ljutomer,
Moravske Toplice, Murska Sobota, Odranci, Puconci,
Radenci, Razkrižje, Rogašovci, Sveti Jurij, Šalovci,
Tišina, Turnišče, Velika Polana, Veržej
SI005
Spodnjeposavska
SI006
Gorenjska
SI009
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NUTS 3
Notranjsko-kraška
Area
1456 km2
Municipality
2325 km2
Ajdovščina, Bovec, Brda, Cerkno, Idrija, Kanal, Kobarid,
Miren - Kostanjevica, Nova Gorica, Šempeter - Vrtojba,
Tolmin, Vipava
1044 km2
Divača, Hrpelje - Kozina, Izola/Isola, Komen,
Koper/Capodistria, Piran/Pirano, Sežana
SI00A
Goriška
SI00B
Obalno-kraška
SI00C
Jugovzhodna Slovenija 2675 km2
SI00D
Osrednjeslovenska
SI00E
Statistical Office of the Republic of Slovenia
2555 km2
Bloke, Cerknica, Ilirska Bistrica, Loška dolina, Pivka,
Postojna
Črnomelj, Dolenjske Toplice, Kočevje, Kostel, Loški
Potok, Metlika, Mirna Peč, Novo mesto, Osilnica,
Ribnica, Semič, Sodražica, Šentjernej, Škocjan, Trebnje,
Žužemberk
Borovnica, Brezovica, Dobrepolje, Dobrova - Polhov
Gradec, Dol pri Ljubljani, Domžale, Grosuplje, Horjul, Ig,
Ivančna Gorica, Kamnik, Komenda, Litija, Ljubljana,
Logatec, Lukovica, Medvode, Mengeš, Moravče,
Škofljica, Šmartno pri Litiji*, Trzin, Velike Lašče,
Vodice, Vrhnika
Šentjur* - in the year 2002 municipality Šentjur pri Celju was renamed into Šentjur.
Šmartno pri Litiji* - in the year 2002 Šmartno pri Litiji was formed as a new municipality after
separating from the municipality Litija; the territorial change of municipalities did not affect the
boundaries of statistical regions.
The boundaries of statistical regions have since the year 2000 been based on municipal
boundaries. Statistical regions thus became a part of the hierarchical breakdown of Slovene
territory, which is composed at the following levels: census (spatial) district, statistical
district, settlement, municipality, statistical region. In this way an important situation was
achieved for the statistical system, namely the possibility of fully reliable aggregation of
statistical data from lower to higher territorial levels.
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6. DESCRIPTION OF THE RURAL DEVELOPMENT INDICATORS
6.1. DEMOGRAPHY – MIGRATION
Demography statistics in the Rural Development Project are reported for 3 of the 4 foreseen
indicators. Missing are data on the Net-migration indicator. They are not reported due to the
fact that data on international migrations of foreigners by age are available only at national
level. Furthermore, no data on internal migrations of foreigners in Slovenia are available.
Only data on international and internal migrations of citizens of Slovenia, by age and sex are
available by statistical regions. Due to the fact that foreigners represent the majority of
migrants in Slovenia and that no data on internal migrations of foreigners in Slovenia are
available, it is considered that only data on migrations of nationals might be misleading.
Other data are reported according to both territorial approaches as shown in Table 3.
Table 3: Availability of demographic reported data according to territorial approaches
I. Demography – migration
OECD concept
DU concept
1. Population change
+
+
3. Natural population change
+
+
4. Age structure changes
(+) = available, (-) = unavailable
+
+
6.1.1. METHODOLOGICAL EXPLANATIONS
The sources of data, collected in the Rural Development Project, are:
- annual statistical surveys on births, deaths and migration, conducted by the Statistical Office
of the Republic of Slovenia;
- records on citizens of the Republic of Slovenia: Ministry of the Interior - Central Population
Register (CPR);
- records on foreigners, refugees and persons under temporary protection in the Republic of
Slovenia: Ministry of the Interior – Administrative Internal Affairs Directorate;
- Mortality Database of the Institute of Public Health of the Republic of Slovenia.
Data collection
SORS collects data on births and deaths on forms filled in by employees of administrative
units of the Departments of Internal Affairs based on entries and records in the register. Data
are collected according to place of event, where results of surveys are published by permanent
or temporary residence, i.e.:
- residence of mother or father at childbirth,
- the last residence of the dead person at death.
Data on deaths are taken over on electronic media from the Institute of Public Health of the
Republic of Slovenia. The Institute collects these data in the Mortality Database.
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Table 4: Data sources of the Rural Development Demographic Statistics
I. Demography
– migration
1. Population
change
3. Natural
population
change
4. Age structure
changes
Source of data
administrative
administrative
and other
(demographic
questionnaires on
births and deaths)
administrative
Description of the source
Citizens: Ministry of the Interior - Central Population
Register
Foreigners: Ministry of the Interior – Administrative
Internal Affairs Directorate
Births: Statistical Office of the Republic of Slovenia,
Ministry of the Interior - Central Population Register
Deaths: Statistical Office of the Republic of Slovenia,
Ministry of the Interior - Central Population Register,
Institute of Public Health of the Republic of Slovenia
Citizens: Ministry of the Interior - Central Population
Register
Foreigners: Ministry of the Interior – Administrative
Internal Affairs Directorate
Data coverage
Annual statistical surveys include births of live-born and stillborn children whose mothers or
fathers are residents of the Republic of Slovenia. Data on births include also events which
took place in other countries and from where SORS later acquired data on persons who had
residence in Slovenia.
The collected data cover all deaths which happened in Slovenia and for the population of the
Republic of Slovenia who died in a foreign country. Results are shown only for dead persons
who are residents of the Republic of Slovenia.
6.1.2. DEFINITIONS
The Central Population Register is a collection of data on population registered as permanent
residents in Slovenia, irrespective of whether they are temporarily living abroad and whether
they gave notice of departure in the municipality of their permanent residence.
Definitions on population
According to the present statistical definition, population of Slovenia consists of:
 citizens of the Republic of Slovenia with permanent residence in Slovenia, excluding
those who went abroad for more than three months and gave notice of their departure at the
administrative unit of their permanent residence,
 foreigners with issued permission for permanent residing in the Republic of Slovenia,
who have a registered permanent residence,
 foreigners with issued permission for temporary residing in the Republic of Slovenia,
who have a registered temporary residence,
 foreigners with a valid work permit or a business visa, who have a registered temporary
residence in Slovenia,
 persons according to the Asylum Act to whom the asylum and refugee status were
granted in the Republic of Slovenia (refugees).
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Citizens of the Republic of Slovenia are persons with Slovenian citizenship and permanent
residence in Slovenia.
Citizens of the Republic of Slovenia temporarily residing abroad are citizens of the
Republic of Slovenia with registered permanent residence in Slovenia who have been abroad
for more than three months and gave notice of their departure at the administrative unit.
Citizens of the Republic of Slovenia, excluding citizens temporarily residing abroad, are
citizens of the Republic of Slovenia with registered permanent residence in Slovenia,
excluding persons who have been abroad for more than three months and gave notice of their
departure at the administrative unit.
Foreigners are:
– persons with foreign citizenship and permission for permanent or temporary residing in the
Republic of Slovenia, who have a registered residence in Slovenia,
– persons with foreign citizenship and valid work permit or business visa, who have a
registered temporary residence in Slovenia,
– persons without established citizenship or without citizenship, who have a registered
permanent or temporary residence in Slovenia,
– persons according to the Asylum Act to whom the asylum and refugee status were granted
in the Republic of Slovenia (refugees).
Residence is a settlement in which a person has his/her registered permanent or temporary
residence; permanent residence is a settlement in which a person settled or registered with
the intention of living there permanently; temporary residence is a settlement in which a
person temporarily lives or is temporarily registered and is, as a rule, outside the settlement of
permanent residence.
A live-born is a child who immediately after birth showed signs of life (breathing, beating of
the heart, muscle twitch), even though only for a short period. Duration of pregnancy is not
important.
A dead person is one whose evidence of life permanently disappeared any time after he/she
was live-born.
Age is the period of time a person has lived from his/her birth to the moment of observation
(time cross-section) or until the observed event (birth of a child, death). Data on age are
calculated from the date of birth of the individual and the date of event or the data on the
number of residents (31 December).
Age is specified in two ways:
− by completed years of age,
− by year of birth.
In most cases age is expressed by completed years of age in:
− years of age,
− five-year age groups,
− selected age groups.
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Table 5: Data collected in the Demography-Migration field
Issue
Indicator
Variable
Population
change
Change of total
population
Population year 1
(Pop 1)
Population year 2
(Pop 2)
Birth/death
ratio and %
change
Total births year 1
Total births year 2
Natural
population
change
Age structure
changes
Age structure
year 1 by
gender
Age structure
year 2 by
gender
Total deaths year 1
Total deaths year 2
Formula
Comments
Data are represented
by absolute figures
Pop2  Pop1
∙100 and are calculated for
Population change =
Pop 1
the Population
Change indicator
Birth/ death ratio =
% change =
Total births
Total deaths
Birth/deat h ratio 2
∙ 100
Birth/deat h ratio 1
Age groups:
- 0 – 4 years
- 5 – 14 years
-15 - 24 years….
- 55 – 64 years
- 65 or more
All by gender
Data are represented
by absolute figures
for total births and
deaths, calculated for
the Birth/death ratio
and indicator of
percentual change
Data are represented
by absolute figures
Data quality
good
good
good
Since data of the demographic statistics are of good quality, expenses for the improvement of the quality of the data are not applicable. Data on
migrations of foreigners are planned to become available in the near future. Since they are obtained from an external source, it is impossible to
predict costs.
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6.2. ECONOMY – HUMAN CAPITAL
The Economy-Human capital issue is completely covered with data considering the OECD
concept; in case of the DU concept, only data on regional GVA are missing.
Table 6: Availability of reported data on the Economy–Human capital field according to
territorial approaches
II. Economy-Human Capital
OECD concept
DU concept
1. Forms of employment -Self employed
+
+
2. Importance of different sectors
+
+
3. Importance of public sector
+
+
4. Capacity of collective tourist accommodation
+
+
5. Occupancy of collective tourist accommodation
+
+
6. Weight of manufacturing
+
-
7. Weight of tertiary sector
+
-
8. Relative changes of unemployment
+
+
9. Human capital
+
+
10. Potentially available human resources
+
+
11. Relative changes of employment
Comment: (+) = available, (-) = unavailable
+
+
The description of indicators of the Human capital field is in our report divided into two parts;
the first part contains Economy-Human capital indicators (II/1-II/3 and II/8-II/11), which are
reported twice, namely according to different sources. One source is in the project required
Labour Force Survey and the other is a national statistical source – the Statistical Register of
Employment (both data are on the same worksheet; survey data are shown in the upper table
followed by register data). In the second part of this chapter the description of the indicators
from other sources and subject domains are represented.
6.2.1. Indicators based on the Labour Force Survey
6.2.1.1. METHODOLOGICAL EXPLANATIONS
Data that describe the situation and changes on the Slovenian labour market are in the Rural
Development Project provided from two different sources that are official in Slovenia. One is
the Labour Force Survey (LFS) and the other is the register called the Statistical Register of
Employment (SRE). We decided to provide data from both sources because we have foreseen
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that data on LFS would be less accurate since they are estimated on only around 1% of total
population. The sample size is namely made in order to provide sufficiently accurate
estimates at NUTS 2 level, which is in accordance with the regulation.
Both types of the data focus on the population in the age group 15–64 years. There is one
general difference in the data from above mentioned sources: LFS data are annual averages
for each year while register data are extracted on the last day of the reference year (31
December)
In the following text both sources are briefly described with emphasized basic and important
differences between them.
Labour Force Survey
The Labour Force Survey is the most comprehensive official household survey in Slovenia. It
aims to collect data on the situation and changes on the Slovenian labour market. It provides
data on size, structure and characteristics of the Slovene labour force and inactive population.
The Slovene LFS is carried out in accordance with the guidelines of the International Labour
Organisation and Eurostat, so that it is internationally comparable. Since 1997 it has been
carried out quarterly. The first results of LFS data are published two months after the data
collection is finished.
Observation units are all individuals usually living in the selected households. A household is
a single person or a group of persons, who live together and share expenses related to
common living and eating, etc. Temporarily absent members of the households without any
other habitual residence are also included in the household. The survey does not include persons
who live in institutions (army, hospital, prison, etc.) for a total period exceeding six months,
students living away from home and persons living permanently or temporarily in other countries.
The Labour Force Survey is a rotating panel survey conducted continuously through the year.
Each household is interviewed five times according to the rotation pattern 3-1-2 (households
are interviewed for three consecutive quarters, excluded for one quarter, and included for
another two consecutive quarters). The panel part of the sample are households included in
the survey for the second, third, fourth and fifth time. It represents four fifths of the sample.
Households included in the survey for the first time represent the new part of the sample.
A stratified simple random sample is used. In the reference years 2000-2004 the average panel
part of the sample included more than 21 000 households.
Table 7: Size of the Labour Force Survey sample in the reference years
Year
2000
2001
2002
2003
2004
Panel part
21189 households
21323 households
21778 households
22555 households
21082 households
New part
8112 households
8020 households
7981 households
7944 households
8128 households
Stratum allocation is proportional to population distribution by statistical regions and type of
settlement. Possible disproportionate distribution of surveys in different time periods is
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corrected with weighting. Labour Force Survey estimates are based on the probability sample
and are subject to sampling errors.
It has to be taken in consideration that data presented in the Rural Development Project at
NUTS 3 level are less accurate since they are collected in order to present estimates at NUTS
2 level, which is temporarily the territory of the whole Slovenia.
Statistical Register of Employment
The Statistical Register of Employment (SRE) is a collection of data on persons in
employment. It was set up on the basis of data collected by the census of workers in
associated labour as of 31 December 1986. It is regularly updated with data from monthly
surveys (Monthly Report on Earnings and Persons in Paid Employment in Enterprises,
Companies and Organisations), i.e. with registration data for introduction and monitoring of
records on retirement and disability insurance and health insurance, records on stipulated
employment.
The SRE covers persons in paid employment (insured) in the Republic of Slovenia by legal
persons and persons in paid employment by natural persons, individual private entrepreneurs,
own account workers, farmers, registered unemployed persons and pensioners.
The SRE contains employee’s personal identification number, date of registration of
insurance, date of cessation of insurance, date of change of data, identification number of the
business entity or business unit, level of school education, level of highest school graduation,
level of professional attainment, level of professional skills, occupation, shift work,
employment relationship, working hours (weekly), the basis for insurance, citizenship, etc.
6.2.1.2. BASIC DEFINITIONS AND CONCEPTS
Definitions used in the Labour Force Survey

Working age population comprises all persons aged 15 years and over.

The labour force covers persons in employment and unemployed persons.

Persons in employment are those who during the last week (from Monday to
Sunday) prior to the interview did any work for payment (in cash or kind) or family
gain. Persons who were not working but had a job from which they were temporarily
absent are also included. The same applies to workers on lay-off, persons on maternity
leave and unpaid family workers.
Unpaid family workers are those who were not in paid employment or self-employment
during the last week prior to the interview, but did some work on the family farm, in the
family enterprise or some other kind of family gainful activity; they normally do not
receive regular payment.

Self–employed persons are persons performing economic or gainful activity
(individual private entrepreneurs), own account workers (freelancers) and farmers.
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
Unemployed persons are those who during the last week prior to the interview:
- did not work (not in paid employment or self-employment and did not do any paid
work);
- were actively seeking work (specific steps were taken in the past four weeks to seek
paid employment or self-employment);
- were currently available for work (within two weeks),
- and have already found a work to start in two weeks.

Activity rate represents the labour force as a percentage of the working age
population.

Employment/population ratio represents persons in employment as a percentage of
the working age population.

Unemployment rate represents unemployed persons as a percentage of the labour
force.
Definitions used by registered data

Labour force covers persons in employment and registered unemployed persons.

Persons in employment are persons in paid employment and self-employed persons
who: - have compulsory pension and health insurance or are employed on the territory
of the Republic of Slovenia;
- are at least 15 years old and not retired.
Employment can be permanent or temporary, full time or part time.
Persons in employment are:
- persons in paid employed by legal persons or in subsidies of foreign enterprises,
elected or appointed holders of public functions, mothers with children working under
special regulations, owners or enterprises who run them personally and are not insured
otherwise, and since 1 January 1999 people performing public works; persons in paid
employment are not persons working by legal persons via copyright contracts,
contracts for work/service and citizens of the Republic of Slovenia permanently
employed in Slovene embassies, on construction sites, etc., abroad.
- persons in paid employment by natural persons, by own account workers
performing their activity as the only or principal occupation, and by natural persons
using supplementary work of other people;
- self-employed persons:
persons performing economic or gainful activity (individual private entrepreneurs) and
own account workers performing their activity as the only or principal occupation (e.g.
independent researchers).

Registered unemployed persons are persons aged 15 to 60 years (women) or 15 to
65 (men) whose health enables them to accept employment, are registered at the
employment office, are actively seeking employment and are willing to accept
employment which corresponds to their educational attainment, knowledge and
capabilities within 14 days. In addition, they are not retired, imprisoned for more than
6 months, already employed, owners or co-owners of enterprise with which they
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created income in the last calendar year and which they could use to support
themselves or income they created does not exceed the guaranteed earnings.
Registered unemployed persons do not perform independent activity as a regular job,
are not owners, sharecroppers, renters or other users of agricultural or forest areas or
real estate which can provide a living.

Activity rate represents labour force as a percentage of the working age population.

Employment/population ratio represents persons in employment as a percentage of
the working age population.

The registered unemployment rate is the percent of registered unemployed persons
in the labour force. The registered unemployment rate by municipalities and
administrative units of Slovenia is shown by permanent residence of persons in
employment and registered unemployed persons.
The differences between registered data and data obtained by LFS originate from different
data collection methods and definitions. However, they show the problem from different
aspects, which enable greater reliability in ascertaining and solving problems of employment
and unemployment.
Methodological differences between register data and data obtained by statistical
surveys appear due to:
1. categories included in persons in paid employment
Register data cover only persons in paid employment with employment contracts while the
Labour Force Survey also covers unpaid family workers and persons working under contracts
for work or for direct payment, i.e. persons who in the week (from Monday to Sunday) before
the interview performed any work for payment (in money or in kind), profit or family gain.
Persons who were not working but had a job from which they were temporarily absent are
also included.
2. definition of unemployed persons
Registered unemployed persons are persons who are registered by the Employment service of
Slovenia and fulfil all criteria defined by the employment service.
According to the Labour Force Survey unemployed persons are persons who in the week
(from Monday to Sunday) before the interview did not perform any work for payment (in
money or in kind), profit or family gain but have been actively seeking work in the last four
weeks before the interview and are prepared to accept it in two weeks. Unemployed persons
are also those who found work and will start working shortly after the interview.
LFS and SRE also differ in the source of data, reporting period and observation period.
Both survey data are obtained by the Labour Force Survey while register data on persons in
employment are based on the Statistical Register of Employment and data on unemployed
persons from the Register of Unemployed Persons, which is kept by the Employment Service
of Slovenia. The reporting period for survey data refers to the activity of the respondent in
the week before the interview (from Monday to Sunday), while register data are extracted on
the last day of the month.
Differences are also in the observation period. Register data are extracted on the last day of
the month while the Labour Force Survey is carried out quarterly.
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6.2.1.3. Data about educational level of population
There are two major sources for data on Human capital or education at regional level - the
Labour Force Survey and the 2002 Population Census. Both are prepared on the basis of the
International Standard Classification of Education (ISCED) of 1997.
Data collected with the LFS concern the highest level of education attained (educational
attainment) as well as recent or current participation of the population in education and
training.
Census data cover the whole population. They present the highest level of publicly recognised
education that a person obtained by finishing schooling in publicly recognised programs in
regular school or schools replacing regular schools (part-time education, long-distance
education, etc.), courses, and examinations or in other ways in accordance with regulations on
obtaining publicly recognised education. The highest level of education is proven by a public
document (school certificate, diploma, etc.).
The table below shows the main methodological approaches used to obtain the described
Economy–human capital indicators in the Rural Development Project.
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Table 8: Data collected in the Economy–Human Capital field, based on the Labour Force Survey
Issue
Forms of
employment
Importance of
different sectors
Importance of
public sector
Indicator
Variable
Formula
Percentage of selfemployed persons
(SEP)
Employment in
agriculture and
forestry, secondary
and tertiary sector by
gender
-Number of self-employed persons
-Total of employed persons by
gender
Data are represented by absolute
Number of self  employed
SEP 
 100 figures, the percentage of selfTotal of employed
employed is also calculated
- Average total employment in
agriculture and forestry, secondary
and tertiary sector
-Total employed persons by gender
Percentage of
- Employment in public sector by
employment in public
gender
sector by gender (EPS) - Total employed persons by gender
Relative changes
of
Rate of unemployment
unemployment
(RU)
Educational level of
Human capital
population by gender
and age
Potentially
available human Activity rate by
resources
gender (AR)
- unemployed persons
- Active population by gender and
by age group: 15-24; 25-54; 55-64
Number of people with a certain
level of education by gender and
by age group: 15-24; 25-54; 55-64
-Active population
- Population of the working age
(15-64y) by gender
Relative changes Rate of employment
of employment (RE)*
- employed person
- Active population by gender
Comments
Data for NACE A, NACE C-F
and NACE G-Q are represented
by absolute figures
EPS 
RU 
AR 
Employed in public sec tor
∙100
Total of employed persons
Unemployed persons
 100
Active population
Active population
 100
Population of working age
RE * 
Employed persons
 100
Active population
Data are represented by absolute
figures, the percentage of
employed in the public sector
according to NACE L-M is also
calculated
Data are represented by absolute
figures, the unemployment rate is
also calculated
Data are represented by absolute
figures
Data are represented by absolute
figures, the unemployment rate is
also calculated
Data are represented by absolute
figures, the employment rate * is
also calculated
Rate of employment* - generally the rate of employment represents persons in employment as a percentage of the working age population and
Employed persons
the formula used is RE 
 100 ; since the definition of the rate of employment in the Rural Development Project differs
Working age population
form usually used, we recommend to rename the Rate of employment indicator, e.g. to employment/active ratio.
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Some specific differences between LFS and SRE figures collected in the Rural Development
Project are also presented in the following table where the quality of data is also discussed.
Table 9: Data quality and differences between data figures
Issue
Forms of employment
Importance of different
sectors
Importance of public
sector
Comments
Figures for register data are lower
because LFS data also include
unpaid family workers
Difference in figures in NACE A
due to coverage of farmers and
unpaid family workers in LFS data
Data according to NACE L-M
activities; figures of data from both
sources are comparable
Differences in figures are due to
different methodological approaches
of active population
Data quality
”s”: mainly good; some
small regions medium
“o”: good
”s”: mainly good; some
small regions medium
“o”: good
”s”: mainly good; some
small regions medium
“o”: good
”s”: low
“o”: good
”s”: mainly good;
estimates for ISCED 5
Differences in figures are due to the
and 6 are in some
coverage of data
Human capital
regions of low quality
“c”: good
Differences in figures are due to
Potentially available
different methodological approaches ”s”: good
human resources
of active population
“o”: good
Differences in figures are due to
Relative changes of
different methodological approaches ”s”: good
employment
of active population
“o”: good
Comment: “s”= survey data, “o”=other data (statistical register)
Relative changes of
unemployment
It is important to take into account the fact that the accuracy of estimates of all LFS data in the
Rural Development Project was calculated directly, with SAS survey means procedure, which
uses Taylor linearization method and differs from accuracy of official Labour Force Survey
estimates, based on a model. This can be the reason for some inconsistency between accuracy
measures (signs) for Labour Force Survey estimates and accuracy measures for Rural
Development estimates, but we don't expect larger deviations. The explanation of accuracy
measures (signs) is in the legend below the tables.
Costs needed to attain accurate data at NUTS 3 level were not computed. Considering
estimations that were made to assure accurate data in the case of dividing Slovenia into 2
regions at NUTS 2 level, the survey sample will have to be enlarged by 50%. This
enlargement is expected not to infect the quality of data at NUTS 3 level, because of few
small regions (Zasavska, Koroška, Spodnjeposavska, Notranjsko-kraška, Obalno-kraška) that
would have to be completely covered to provide accurate data.
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6.2.2. Other Economy–Human capital indicators
In this part two sets of indicators are shown which represent more economically oriented
indicators – tourism statistics and regional accounts.
6.2.2.1. Tourism statistics
Data on the Capacity of collective tourist accommodation indicator are reported for three
variables – number of establishment, number of bedrooms and number of bed-places. First
available data on number of establishment are from the year 2003, while other data cover all
reference years. The same applies to the indicator Occupancy of collective tourist
accommodation.
Data on the Capacity of collective tourist accommodation and on the Occupancy of collective
tourist accommodation are collected by a monthly survey which observes tourist activities of
all organisations offering accommodation to tourists. Sources of data are results of the survey
on arrivals and overnight stays of tourists carried out by the Statistical Office of the Republic
of Slovenia. Data are provided by business entities offering accommodation to tourists. They
report on accommodation capacity in individual months, and on the number of tourist’s
arrivals and overnight stays. The survey is based on full coverage.
Basic definitions
Tourists are people who, in a location outside their place of permanent residence, spend at
least one night in a hotel or some other tourist accommodation.
Tourist accommodations are divided into types of tourist accommodations according to the
Rules on Criteria and Method for the Categorization of Accommodation Establishments and
Marinas (OJ RS No. 29/97, 51/98 and 46/2005). Types of tourist accommodations are: hotels,
motels, boarding houses, overnight accommodations, inns, apartments, camping sites, tourist
farms with accommodation and private accommodations (i.e. letting of room, apartments and
houses to tourists). All these tourist accommodations must be categorised, i.e. for a certain
type of offer they must have the category assigned by the number of stars (for tourist farms
the number of apples is used), which denotes the quality of their offer according to the above
mentioned regulation.
Uncategorised and miscategorised tourist accommodations were classified as other
accommodation facilities. In addition to the mentioned tourist accommodations for which
categorisation is compulsory, we also collect data for tourist accommodations that are not
classified by the above mentioned regulation. These are mountain huts, company vacation
facilities, vacation facilities for youth, other vacation facilities and temporary accommodation
facilities (student residence and boarding schools that are available to tourists during the
holidays).
Accommodation capacity is expressed by the number of rooms and beds available to tourists
in individual months. Beds are permanent and auxiliary.
Permanent beds are beds which are regularly prepared for letting to tourists.
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6.2.2.2. Regional accounts
Regional accounts are a subsystem of national accounts where specified spatial units – regions
are the main object of survey. Concepts used in regional accounts are the same as those used
in national accounts. Regional GVA figures are calculated by an indirect method where
national GVA figures by activities are allocated to regions by appropriate keys.
Regional GVA is obtained by regionalizing the national accounts GVA, actually their income
components, for the respective activity by keys, which are mostly wages and salaries,
employees and some other keys for special cases.
Income components of GVA that are regionalized are:
- (A) compensation of employees of those working at legal units,
- (B) compensation of employees of those working at households,
- (C) gross operating surplus
- (D) gross mixed income.
For each above mentioned income component the most appropriate key is used; they are
divided into primary and other keys. Primary keys are:
- wages and salaries of employees at legal units and are used as a key for A and C
income component of GVA,
- weighted number of employees at households is used as a key for B; the weighting is
carried out by the average wages and salaries at legal units in the same region,
- weighted number of self-employed and employees at households is used as a key for
D, the weighting is carried out by the average wages and salaries at legal units in the
same region.
Other keys are used when they are better than any of the above listed primary keys.
The calculation covers all activities following the concept of production by the 1993 System
of National Accounts (SNA 93) and the 1995 European System of Accounts (ESA 95).
Calculations are made of various sources - national accounts data, the Statistical Register of
Employment, the Monthly Report on Earnings by Legal Entities, the Agricultural Census,
land cover statistics, industry statistics and dwellings statistics.
Regional GVA figures are published for 12 NUTS 3 regions and 13 activities 22 months after
the reference period. The last year for which data are published is 2003; data for 2004 will be
available in December 2006.
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Table 10: Data collected in the field of other Economy–Human capital indicators
II. Economy –
Human Capital
4. Capacity of
collective tourist
accommodatio
5. Occupancy of
collective tourist
accommodation
6. Weight of
manufacturing
7. Weight of
tertiary sector
Source of
data
survey
census
survey
other
(statistical
register)
Description of the
source
monthly reports
from enterprises,
companies and
organisations
offering
accommodation
Survey on Wages
and Employment,
dwellings statistics,
industry statistics,
SRE
Data
quality Comments
First available data on
number of establishment
are from the year 2003
good
Data are reported for all
reference years
Data cover NACE D
good
Data cover NACE G-P
because figures for
activities in NACE Q are
not calculated separately
6.3. Economic structure & Performance (Agriculture and forestry)
Collection of agricultural statistics in the Rural Development Project contains 3 indicators:
Structure of agricultural businesses, Weight of primary or agricultural sector (GVA) and
Productivity of the agricultural holdings. They are provided at different territorial levels and
cover different reference years. Detailed description follows in the last section of the report.
Table 11: Availability of reported data on the Economy–Human capital field according to
territorial approaches
IIa. Economic structure & Performance
(Agriculture and forestry)
OECD concept
DU concept
NUTS 2
1. Structure of agricultural businesses
+
+
-
2. Weight of primary or agricultural sector
+
-
-
3. Productivity of the agricultural holdings
Comment: (+) = available, (-) = unavailable
-
-
+
6.3.1. Methodological explanations
Structure and productivity of agricultural holdings
Data on the structure and productivity of the agricultural holdings are based on the 2000
Agricultural Census and on the 2003 Farm Structure Survey while GVA of agriculture is
based on more sources: SRE, the Agricultural Census, land cover statistics, Survey on Wages
and Employment, dwellings statistics.
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The Farm Structure Survey (FSS) is one of the basic statistical surveys in the field of
agriculture. In accordance with EU regulation it is conducted as a census every 10 years.
Between censuses it can be conducted as a sample survey.
Observation units are family farms satisfying the criteria of EU comparable threshold and all
agricultural enterprises and co-operatives. Data on agricultural enterprises and co-operatives
were collected by questionnaire by post. The reference date to which the Farm Structure
Survey refers is 1 June 2003. Data on family farms were collected by interviewers.
Data on family farms are obtained on a sample of 15,000 family farms, which is around 20%
of the total number of family farms. The sampling frame of the survey was a census list of the
2000 Agricultural Census updated with the results of the surveys carried out after the year
2000. There were altogether 85,854 family farms included into the sampling frame.
The sample design is stratified simple random sampling. Family farms were stratified
according to 20 criteria. If the family farms satisfied one of the criteria for stratum one, they
were allocated into this stratum. On other family farms criteria for stratum 2 were applied and
there were allocated into stratum 2 or 3 (if they did not satisfy criteria for stratum 2). Those
family farms which did not satisfy criteria for stratum 3 were allocated into stratum 4. Family
farms from stratum 1 (large family farms) were selected with certainty. In strata 2–4
allocation was defined proportional according to the number of family farms. Afterwards
family farms were selected systematically. The final size of the sample was 15,000 family
farms. The obtained data were weighted due to unequal probability for selection and nonresponse.
According to Regulation 571/88 on the organisation of Community survey on the structure of
agricultural holdings, Member States have to provide reliable results at the level of NUTS 2
when sample surveys are carried out. That is why estimations at NUTS 3 level are unreliable.
Another problem is also that data confidentiality has to be respected. That means that
individual figures for less numerous classes stay hidden because of increased risk of
disclosure of private information of individuals and business secrets of business units.
Because of the mentioned regulation cost needed to obtain reliable data were not carried out.
Legislation in the field of crop production and animal production does not require collecting
data at NUTS 3 level. The indicator Productivity of the agricultural holdings could not be
estimated from figures based on NUTS 2 level.
Regional GVA in agriculture and forestry
The calculation of this indicator uses the same top-down method described in the chapter on
Economy-Human capital indicators. NACE A01 section uses a key made up of combination
of the number of farmers and the area of the agriculture, both based on data from the
Agricultural Census (and from other sources for the updates) to regionalize the gross mixed
income and the compensation of employees of those working at households in this industry.
The wages and salaries of employees of those working at legal units are used for
regionalization of the compensation of employees of those working at legal units and of the
gross operating surplus. By adding up all of these income components GVA per region is
obtained.
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For the section A02 the forest area from the Land Cover Statistics is used as a key to
regionalize the national gross mixed income and compensation of employees of those working
at households. The wages and salaries of employees working at legal units are used for the
regionalization of the compensation of employees of those working at legal units and of the
gross operating surplus. By adding up all of these income components GVA per region is
obtained.
Data are estimated to be of medium quality and are shown for the whole NACE A section. It
is considered that working data used for calculation of forestry are of low quality and because
separate regional data for hunting are not available, the whole section A is provided
(including hunting and forestry).
Data on GVA in the agriculture and forestry sector for 2004 will be available in December
2006.
6.3.2. Definitions and other explanations
Agricultural holding is a single unit, both organisational and operating, of agricultural area,
forests, buildings, equipment and labour force, which has a single management and which is
engaged in agricultural production.
Agricultural holdings are following the Farm Structure Survey typified according to a uniform
classification of agricultural holdings in EU Member States by the economic size and by the
type of agricultural production. The procedure of typology calculation is defined by
Commission Decision No 85/377/EEC establishing a Community typology for agricultural
holdings.
For each agricultural product the coefficient of standard gross margin (SGM) is calculated,
which expresses the difference between the potential income and specific variable costs of
production per hectare or per head of livestock. The economic size of an agricultural holding
is assessed by summing up the products of SGM values of individual cost items and the extent
of their production. The economic size is expressed in ESU (European Size Unit), which
equals EUR 1200.
The extent of work is expressed in annual work units, i.e. the relationship between the
number of hours worked on the farm in one year and the extent of work done by one fully
employed person in one year (1,800 hours), which is being used by the national labour force
statistics.
The calculation of the AWU takes into account the total annual labour input on agricultural
holdings. In addition to work done by the holder, other family members and people regularly
employed on the agricultural holding, hired labour (either in the form of services performed
with machinery or in the form of seasonal and occasional work) is also covered.
The persons on family farms are divided as follows:
- family members (holder and other family members)
- regularly employed persons.
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Table 12: Data on reported indicators in the Economic structure & performance of
agriculture field
IIa. Economic structure &
Performance (agriculture
and forestry)
Structure of agricultural
businesses
Weight of primary or
agricultural sector
Source of
data
census
survey
census
survey
other
(statistical
register)
Description of the source
Agricultural Census 2000
Farm Structure Survey
Data quality
“c”: good
“s”: medium to low
Agricultural Census, land
cover statistics, Survey on
Wages and Employment,
dwellings statistics, SRE
medium
Productivity of the
survey
Agricultural accounts
agricultural holdings
Comment: “s”= survey data, “c”=census data;
* quality is expressed for data of NUTS 2 level
good*
6.3. ACCESSIBILITY TO SERVICES – INFRASTRUCTURE
This field contains 3 indicators. Data on the Transport system were not collected since it was
stated that Eurostat will provide data needed to express the indicator.
Table 13: Availability of reported data on the Economy–Human capital field according to
territorial approaches
III. Accessibility to services –
infrastructure
OECD concept
DU concept
1. Availability of roads/rails
data will be
provided by Estat
data will be
provided by Estat
2. Supply with schools
+
+
+
Comment: (+) = available, (-) = unavailable
+
3. Proximity to primary schools
The Supply with schools indicator was calculated. Data on the number of schools represent
single site elementary schools, central elementary schools and their subsidiaries, elementary
schools and departments with special curriculum for children with special educational needs.
Data on elementary schools for youth are collected by regular annual statistical surveys with
the ŠOL-O questionnaire, which is answered by observation units (elementary schools) on the
basis of their records. Data collected for the Rural Development Project refer to the beginning
of the school year (reference date is 15 September).
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For the indicator expressing Proximity to primary schools we collected coordinates of primary
schools for 2004 and data on total population of all LAU 2 extracted on the last day of the
reference year.
Coordinates are provided for all primary schools reported in the Supply with schools indicator
for the year 2004. They consist of 804 coordinates for single site elementary schools, central
elementary schools and their subsidiaries, 44 coordinates for elementary schools with special
curriculum for children with special educational needs and 16 coordinates for departments
with special curriculum for children with special educational needs, which are included in
single site elementary schools. Since they are located at the same address, they are reported
twice (16 coordinates). The same occurs in case of two foreign language schools in Obalnokraška region (LAU 2 codes are 50 and 90), which are located at the same address as another
primary schools.
Coordinates are provided for 864 schools, which is for one school more than reported in the
year 2004. The reason for it is that the Statistical Office of the Republic of Slovenia could not
gain data from one of working schools in the school year 2004/2005.
Another important fact is that in case of two elementary schools coordinates could not be
provided because the house centroids at their addresses do not exist (schools with LAU 2 code
65 and 73).
The set of coordinates is included in the Data_Rural development.xls file. Coordinates
presenting schools with special curriculum for children with special educational needs are at
the end of the table and are marked with yellow colour, where departments with special
curriculum for children with special educational needs are marked with bold font style.
Table 14: Data on Accessibility to services – infrastructure indicators
III. Accessibility to
services – infrastructure
Supply with schools
Proximity to primary
schools
Source of data
survey
administrative
Description of the source
annual statistical survey
Central Population Register
administrative
Register of Spatial Units
Central Population Register
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6.4. SOCIAL WELL-BEING
Social welfare statistics proved to be the most problematical item concerning the Rural
Development Project. First of all, we could not provide data that were supposed to be
collected, i.e. data based on EU-SILC. Namely, the aforementioned survey was first carried
out last year, so that most data that will be available in November 2006 will not cover
reference years foreseen in the Rural Development Project.
The second problem is that we could not provide all the necessary data. Our social welfare
collected data are based on an alternative survey – the Household Budget Survey, which does
not contain any questions on accommodation and housing conditions. Only questions on
durables are included. The methodology of the Slovene Household Budget Survey follows
Eurostat’s recommendations, so that data are comparable. Published results of the survey in
the period of one year are based on combining the sample of three consecutive years. Data are
calculated to the middle year, which is used as the reference year.
The third thing that could not be fulfilled is the territorial demand, which means that it was
impossible to follow neither one of geographical classifications concepts. Therefore all data
on social welfare, except data on GDP, are provided at NUTS 2 level. The reason for it is a
great uncertainty of data estimations at NUTS 3 level. The Household Budget Survey is
namely concentrated on a small number of observation units – only 5220 households were
included in 2003. Some small regions cover a low number of observation units which is why
it is impossible to get accurate estimates.
Table 15: Availability of reported data on the Economy–Human capital field according to
territorial approaches
IV. Social well-being
OECD concept
DU concept
NUTS 2
1. Households disposable income
-
-
+
2. GDP per capita
+
-
-
3. At-risk-poverty rate
-
-
+
4. Quality of life
-
-
-
5. Quality of life
Comment: (+) = available, (-) = unavailable
-
+
Data are provided for the whole NUTS 2 region, because partly calculated estimations were
considered as not enough representative.
In case of data representing regional GDP we provided the GDP per capita indicator because
data on full time equivalents or hours worked are not available.
Costs needed to obtain more accurate estimates at lower territorial level from the current
NUTS 2 level were not carried out for an obvious reason. Namely, present social statistics
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Statistical Office of the Republic of Slovenia
based on the Household Budget Survey are already being replaced by the EU-SILC survey. At
the moment it is impossible to predict the accuracy of the new data but since the sample size
of units observed is much larger than in the pervious Household Budget Survey, expected
results could be better (EU-SILC includes around 13 500 households).
Definitions and explanations
Households disposable income
Available assets cover all available money assets, own production and benefits in kind at
household’s disposal during the observation period.
Available money assets of a household cover all financial income that was received by
household members in 12 months:
- income from work under employment (wages/salaries and other income of employed
members);
- income from work under contract (including copyright income and payment via a
student employment office);
- income from self-employment (income of entrepreneur, from agricultural holding and
from activity);
- pensions with supplements;
- other income from social security (child allowance, maternity leave compensation,
social benefits, disability allowance, scholarships, unemployment benefits);
- income from property (income from letting rooms or dwellings, income from patents,
licences and other rights, dividends, interests);
- income from sale (sale of intangible and tangible goods, sale of securities);
- other receipts (winnings at gambling, compensation for nationalised or dispossessed
property, inheritance and legacy, income from life insurance, from insurance
companies);
- financial benefits and gifts (alimony, maintenance allowance, financial gifts).
Value of own production covers value of own agricultural products and goods consumed
within a household during the year (food, beverages, heating). The value of used up
agricultural products and goods is calculated according to average selling prices.
Benefits in kind cover goods that the household received - from employment (employer
covering the household’s expenditure for electricity, cold water, gas, telephone, TV
subscription, vehicle registration, free meals, etc.) and
- other (relatives or friends paying the household’s bills for electricity, cold water, gas,
telephone, TV subscription, vehicle registration,
GDP per capita
Gross domestic products equal the gross value added at basic prices increased by the net taxes
on products.
Regional GDP is calculated by the same top-down method as regional GVA where national
GVA figures by activities and income are allocated to regions by appropriate keys. The
calculation covers all activities following the concept of production by the 1993 System of
National Accounts (SNA 93) and the 1995 European System of Accounts (ESA 95).
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Sources used for the calculation are the same as for regional GVA: national accounts data, the
Statistical Register of Employment, the Monthly Report on Earnings by Legal Entities,
Agricultural Census, land cover statistics, industry statistics and dwellings statistics.
GDP per capita is calculated from regional GDP figures and figures for regional population
number adjusted to quarterly averages. They are calculated according to quarter average for
Slovenia and regional structure of population, extracted on the 30 of June for each reference
year.
At-risk-poverty rate
The at risk poverty rate is expressed in the percentage of persons living in households where
the equivalised total net household income is below the threshold.
The at risk poverty threshold is defined as 60% of median equivalised net income of all
households using the OECD modified equivalence scale. It was calculated into euros by
Eurostat exchange rate.
Table 16: At risk poverty threshold for the reference years
Year
At risk poverty threshold
(EUR)
2000
4148
2001
4359
2002
4582
2003
4742
In order to be able to compare the level of living for different households, we have to take into
account their size and structure. For this reason we use equivalence scales, which take into
consideration the economy of scale. We have used the OECD modified scale and calculated
the income per adult equivalent member. The scale gives to the first adult in the household
weight 1, to every other person 14 or more years old weight 0.5 and to children under 14
weight 0.3.
Quality of life
Data on disposable durables are based on the statement of individual households. The source
is the Household Budget Survey.
In case of households reporting lack of home computers, we managed to get data from the
new annual survey about the usage of Information–Communication Technologies, which
collects information about computer and information literacy of the Slovenian population.
Since the survey was first carried out in 2004, we could provide data only for this year. They
are estimated for Degree of urbanisation areas at NUTS 2 level, because survey data can not
give accurate estimations on figures for regions. For such intention the sample size would
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Statistical Office of the Republic of Slovenia
have to be enlarged from the current 2500 observation units to 10 000 units, which would
increase costs by around SIT 50 million or EUR 208 500.
The survey methodology is harmonised with the Eurostat guidelines.
Table 17: Data on Social Well-being indicators
IV. Social
well-being
Households
disposable
income
GDP per capita
At-risk-poverty
rate
Source of
data
Description of the Data quality
source
survey
Household Budget
Survey
census
survey
other
(statistical
register)
Agricultural Census,
land cover statistics,
Monthly Report on
Earnings by Legal
Entities, dwellings
statistics, SRE
survey
Household Budget
Survey
Household Budget
Survey; Information
Quality of life
survey
Communication
Technologies Survey
* Quality is expressed for data of NUTS 2 level
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good*
good
good*
good*
Comments
Data at lower
territorial level
could not be
provided
Figures for
population
present adjusted
figures for
regional
population
Data at lower
territorial level
could not be
provided
Data at lower
territorial level
could not be
provided
7. RESULTS OF REPORTED DATA IN THE RURAL DEVELOPMENT PROJECT
Since it was not always possible to provide the coverage of data at NUTS 3 level under
consideration of both territorial approaches, within existing data we made a detailed
introduction of the work results.
Figure 3: Comparison of the number of foreseen and reported Rural Development indicators
30
Comparison of the number of indicators foreseen and
reported in the Rural Development Project
25
5
4
20
2
2
3
3
15
10
11
11
5
4
3
foreseen
reported
0
Demography
Economy-Human capital
Agriculture
Accessibility-Infrastrucutre
Social w ell-being
We managed to cover 92% of the foreseen Rural Development indicators. This figure presents
all provided data at NUTS 3 and NUTS 2 level. Only two sets of data could not be provided,
i.e. data concerning Migration and data concerning Quality of life – accommodation and
housing conditions.
The reported indicators are shown for different territorial levels. The best results were
achieved in the Accessibility to services–infrastructure field where data are provided for both
geographical levels considering OECD and “Degree of Urbanisation” concept. The next well
covered field is Economy–Human capital where data are also reported for all indicators. The
percentage of covered foreseen indicators is very high (91%). Only data for GVA do not
satisfy both territorial concepts – they are expressed for whole NUTS 3 regions. Data on
Demography are also given in the foreseen form – OECD and DU. In case of Agriculture
statistics, data are given only for certain years (2000 and 2003). Complete coverage with both
territorial approaches is provided in case of census data for Structure of agricultural holdings,
data for 2003 are estimated only for the whole NUTS 3 regions. Data on Productivity of
holdings are given only at NUTS 2 level.
The situation on the Social well-being field is at least satisfying. No indicator could be
provided at both territorial levels. Only GDP per capita is shown at NUTS 3, other figures are
Rural Development Final Report
Statistical Office of the Republic of Slovenia
shown for NUTS 2 level. Beside that, figures for the Quality of life indicator could not be
given since they had not been collected until 2004 when the EU-SILC survey was first carried
out in Slovenia.
Figure 4: Territorial levels of reported indicators
Territorial levels of reported data by fields
20
1
2
18
16
2
14
2
1
12
10
11
8
9
6
4
2
3
3
OECD
DU
3
1
0
Demography
Economy-Human capital
Agriculture
NUTS2
Accessibility-Infrastrucutre
Social w ell-being
To sum up, complete territorial coverage (data shown by both geographical concepts) was
achieved within 60% of all rural development indicators collected by SORS. Data at NUTS 3
level are given in case of 19 indicators which comprise 76% of all rural development
indicators.
Figure 5: Number of reported Rural Development indicators by fields
Number of reported indicators in the RD project by fields
12
10
8
6
4
2
0
Demography
Economy-Human capital
Agriculture
DU
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OECD
AccessibilityInfrastrucutre
Social w ell-being