Final Report - RURALJOBS (New sources of employment to promote the wealth-generating capacity of rural communities)
Executive summary:
TheRURALJOBS project started on 1 February 2008, and the project duration was 33 months.
The research objectives set out in the call for proposals were that the project will identify labour market, demographic and economic trends in rural areas across European Union (EU)-27 and the potential for new sources of employment outside traditional primary and secondary sector activities. It will take into account the European guidelines for employment, technological change and the shift to a knowledge based economy. It will focus on human capital, skills and adaptability, as well as demand for labour in sectors such as the provision of environmental services, recreational amenities and traditional skills. It will examine the interaction between different types of rural area (peri-urban, remote, high environmental/amenity value etc.) and the evolution of labour markets, travel to work areas and changing work patterns. It will identify employment growth areas where rural development programmes can be targeted to increase their contribution to employment creation. The expected impact was defined as follows: 'The project results will allow a better targeting of rural development measures and future evolution of rural development policies in line with the Lisbon Strategy'.
In response to the call, the aim of RURALJOBS was to quantify the employment needs and potentials in different types of pilot areas within contrasting reference areas in seven EU countries, evaluating the effectiveness of past and current policies in addressing these needs and potentials, and by systematic analysis of the results, providing guidelines on the better targeting of future rural development measures. The main objective was to provide a clearer understanding of the factors influencing the employment potentials of different types of rural areas to support the future evolution of rural development policies. This was backed up by the identification of good practice and a support network for implementation.
RURALJOBS implemented the planned tasks and fully achieved its objectives. The project was carried out in seven Work packages (WPs).
WP1 and WP7 started at the beginning of the project and continued throughout the life of the project ensuring the overall project management (WP1) and the dissemination of the project (WP7). Most of the work was carried out in WPs 2-4 during the first reporting period, and the implementation of the tasks in WP5 and WP6 was scheduled in the second period.
The project website was regularly updated during the project period.
Project context and objectives:
RURALJOBS worked on quantifying the employment needs and potentials in different typologies of pilot areas within contrasting reference areas in seven countries, evaluating the effectiveness of past and current policies in addressing these needs and potentials, and by systematic analysis of the results, providing guidelines on the better targeting of future rural development measures.
The main objective was to provide a clearer understanding of the factors influencing the employment potentials of different typologies of rural areas to support the future evolution ofrural development policies. This was backed up by the identification of good practice and a support network for implementation.
RURALJOBS addressed the following community strategic guidelines for rural development:
-improving the competitiveness of the agricultural and forestry sectors;
-improving the quality of life in rural areas and encouraging diversification;
- building local capacity for employment and diversification;
-translating priorities into programmes;
-developing complementarity between Community Instruments.
RURALJOBS had four strategic objectives:
(a) review of employment policies and programmes;
(b) scenarios for new sources of employment according to rural typologies;
(c) recommendations for better targeting of strategies; and
(d) dissemination and mainstreaming.
The consortium consisted of eight partners, one from each of Bulgaria, France, Hungary, Italy, Lithuania, Romania, Spain and United Kingdom.
The seven WPs were:
WP1 - Project management
WP2 - Assessment of labour market policies and programmes
WP3 - Assessment methodologies and indicators
WP4 - Typology for regions
WP5 - New strategies for employment in pilot areas
WP6 - Synthesis of recommendations
WP7 - Dissemination and technical assistance for mainstreaming
The beneficiaries included policy makers at EU, national and regional levels, rural development practitioners including public sector agencies, Small and medium-sized enterprises (SMEs) and trade groups, Non-governmental organisations (NGOs) and academics. The deliverables are: reports on past and current policies and programmes,assessment methodologies, rural typologies, labour market scenarios for pilot areas and regional foresight scenarios; a synthesis of recommendations for future rural development strategies, four regional conferences/workshops; a two-day international conference in Brussels; academic publications; training materials and a up to date website.
The sustainable outcome of RURALJOBS is the greater capacity of actors to better target rural development measures, supported by the RUR@CT network and reference area reports on mainstreaming good practice.
Project results:
WP2 - Assessment of labour market policies and programmes (WP leader: DE)
Task 2.1 - Review of past and current labour market strategies and programmes in the EU (Task leader: DE; participants: all partners)
Deliverable 2.1
Task 2.1 first part
The main findings from the statistical analysis of European rural regions are composed in four parts: specific issues potentially affecting employment rates, comparison and the status of urban and rural regions, differences between EU-15 and post-socialist Non-Member States and results of national-regional reports of RURALJOBS partners. Analysis was performed on NUTS2 and NUTS3 levels based on the general and regional statistics of Eurostat dividing the regions in three categories: predominantly rural, intermediately rural and predominantly urban.
Specific issues potentially affecting employment rates consists of five components: demographic development, employment-unemployment, economic progress of rural areas, education, and Human resources in science and technology and research and development.
In terms of demography the EU needs to solve the problem of negative trends in population and labour force aging. The tendency of natural population change increased the disadvantageous position of rural areas. On the EU level the natural change of the population was negative but the average number of population increased moderately from 2000 to 2006. The source of the increase of the population was the net migration to the EU. One way of solving the problem of decreasing population is to increase the immigration however according to numerous studies the negative tendency cannot be compensated by reasonable immigration.
The urbanisation process continued and the gap in population density increased between PR and PU regions and between PR and IR regions from 2000 to 2006. The ratio of females became more significant mainly because the longer life expectancy of women. The number of females was higher than the number of males in each region type and the difference between the female and male annual average population increased in PU, IR and PR regions from 2000 to 2006.
Analysing the employment and unemployment tendencies significant differences were found in urban and rural areas. While the development of employment was the most intensive in IR regions negative tendencies were experienced in PR regions. Total employment generally increased in PU (4.12%), IR (4.25%) and decreased in PR (-0.74%) regions in the EU from 2000 to 2005.
European regions face the consequences of rapid and unequal development of the service sector. It was found that the ratio of people employed in services PU or IR regions was significantly higher than it was in PR region that suggests that rural people's access to various services is very limited in comparison with the possibilities of inhabitants in PU and IR areas which is an important disadvantage of the rural life. The importance of services accelerated in rural areas.
Employment in agriculture, hunting, forestry and fishing decreased greatly in PU, IR and PU regions of the EU the most significant decline of 16.09% happened in PR regions. Regions with small farm holdings generally correlated with low productivity that decreases the competitiveness of these businesses. The share of employment in industry also decreased.
Employment pattern is changing through age groups and gender. Employment rates of youth and elderly are lower than the employment rate of prime-aged people. Employment rate of prime-aged women is generally lower than employment rate of prime-aged men. Youth unemployment rates are generally higher in every region than prime-age unemployment rates therefore a notable part of the potential of the most active group of the workforce is not utilised.
The structure of employment was different in urban and rural areas of the EU. The ratio of employees was the highest in PU regions and the lowest in PR regions in 2000 and also in 2006. The ratio of self-employed people and family workers showed an opposite tendency with low ratios in urban and higher ratios in rural areas. The rate of family workers was about three times more in IR and PR regions then in PU regions.
The unemployment gap increased between rural and urban areas since the difference in unemployment rates between developed regions and less developed regions have increased, getting lower in developed regions and increasing in less developed regions. Long-term unemployment rate was slightly lower in PR regions (37.66%) than in PU and IR regions (2006).
Economic development was more intense in urban areas than in rural regions since the Gross domestic product (GDP) euro per inhabitant in percentage of the EU average was the highest in PU regions (129.2%), less in IR (84.4%) and the lowest in PR (76.4%) regions (2005). Despite the big differences in GDP between the region-types at the beginning of the period the highest development was realised in the most developed PU areas and the lowest increase happened in PR regions which increased the gap between the urban and rural areas.
Quality in higher education is becoming an essential aspect of education as the number of students increased steeply in many European countries. Demographic trends and rapid technological evolution will require shifts in the structure of employment and more people will work in the education system partly to serve the needs of lifelong learning. Transformative and empowering learning can serve the best interest of modern enterprises therefore higher education has to be transformed to this direction. This transformation can be achieved mainly through the extensive application of lifelong learning in higher education.
The ratio of human resources in science and technology was the lowest in rural areas. The development of human resources in science and technology was rather intensive. The increase of human resources in science and technology was 20.28% in PR regions and 16.15% and 15.63% in PU and IR regions.
Comparison and the status of urban and rural regions illustrate the relation of these region types. Comparison of the three region types based on the selected indexes. It was found that in the majority of cases PU regions differs greatly from IR and PR regions than IR regions differ from PR regions. The following examined indexes can mainly prove the above mentioned differences of PU, IR and PR regions:
-the average and total area;
-natural population change;
-population density;
-GDP per inhabitant at current market prices;
- Gross value added (GVA) at basic prices;
-the share of agriculture, hunting, forestry and fishing, industry and services in GVA;
-income of households;
-employment by economic activity;
-employment by professional status, the division of employees, self-employed and family workers;
-unemployment rates of inhabitants, age of 25 years and over;
-human resources in science and technology as the percentage of total population;
-patent applications to the EPO by priority year per million of inhabitants.
The changes in index values from 2000 to 2006 were not always consistent with the above tendencies the increase or decrease of values were not associated with the region type. Moreover, changes of values did not affect generally the relative positions of PU, IR and PR regions for seven years.
In many cases there were found considerable differences between EU-15 and post-socialist Non-Member States. As a result of the migration and natural population change the population density of former socialist countries (Bulgaria, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia and Slovakia) decreased in PR regions and it decreased also in the majority of PU and IR regions. In other countries the population density increased in all region types except in PR regions of Denmark, Greece, and Portugal and in every category in Denmark.
In eastern European countries' total employment increased in PU regions end decreased in PR regions which suggest that the migration of employees from rural areas to urban areas was a characteristic feature of these countries. In former communist countries, except Poland, PU regions had much more higher rate of human resources in science and technology than IR and PR regions mainly as a consequence of the centralised economy, they inherited. The number of patent applications per million of inhabitants to the EPO by priority year was the highest in EU-15 countries with some exceptions far ahead of the Non-Member States.
The status and tendencies of rural employment and development were analysed by each partner on country and/or NUTS 2 levels. Six partners has reference regions on NUTS 2 level (DE, UOP, BBU, LUA, and AND, CRL), two partners (IAE and INIPA) reported on country level. Agriculture has a diverse but diminishing importance in RURALJOBS' countries and reference regions; however the small average farm size is a general problem which is a barrier to efficient agricultural production. Overall the average farm size increased in the period of 2000 to 2006. In the four EU-15 countries the diversification of the farm activities created new sources of employment for the rural population. Mainly in these countries organic farming went through an evolution and became a profitable business in many regions. In Bulgaria, Hungary and Romania the outworn facilities, out of date technologies and inefficient integration are common reasons of inefficiency of agricultural production.
At large growing employment characterised the labour market of the RURALJOBS countries and the reference regions but the employment rates were below 70% except in Essex. In countries where the net migration was negative emigration was a notable factor of unemployment reduction. The unemployment rates were higher in rural regions with one exception.
Summarising the findings from the statistics it can be stated that despite the negative tendencies in demography the EU has gone through a development in many areas. Improvements were experienced in the employment, income of countries, and higher education in both rural and urban areas although the development was more intensive in urban areas which resulted in an increasing gap in favour of urban regions. This phenomenon makes the problems of rural development and rural employment relatively more important than it was a decade ago.
Task 2.1 second part
Employment is a cross cutting principle in EU policies, not targeted separately to rural areas, although lot of measures along different policies address rural areas and have an effect on rural employment. On the other hand 'mainstreaming' employment policy is an important principle. Article 147 (e.x.article 127) 'The objective of a high level of employment shall be taken into consideration in the formulation and implementation of Union policies and activities' - requires that the employment impact of all community policies must be taken into account, including rural development policy. Member States have the main responsibility for employment and social policy. The EU's role is to be a catalyst for change and modernisation. It supports, accompanies and coordinates government efforts to reshape their employment and social policies.
Sustainable development as a framework
In our understanding it is also important to create a structure between the strategies. Following the changes along globalisation, the results of the Word Summit in Johannesburg in 1992 and also the renewed Sustainable development strategy (SDS) of the EU in which it is stated that 'the EU SDS forms the overall framework' SDS of the EU was taken as the one above all the others in our work. It is also important to find the links between the created strategies. The structure of the strategies and policies examined in our work is drawn up in Figure 1. Employment strategy forms a part of Lisbon Strategy from 2000 and both of these strategies have to be placed under the strategy of sustainable development.
While EU gives guidelines (often in the form of regulation, or using open method of coordination) for development strategies next to different policies, there is not any guideline in the case of SDSs. Also the timeframe of the member states SDSs and the year of acceptance differ between the countries. SDSs of EU countries do not have separate EU funds.
Long-term economic, social and environmental indicators give a better picture of a country's progress than do traditional methods. Measured in sustainable development terms the EU-27 leads the word. This is also underlined by the fact that ten years after Rio, from the about 6 500 authorities, which begun their own local agenda 21 processes more than 5000 are in Europe. These issues from the dimension of sustainability create new possibilities for rural employment. It also has to be taken into consideration that human resources are renewable resources of the economy.
Harmonisation of regional and rural development policies
Along the analysis of the documents the turnout of selected words related to employment (adaptability, knowledge-based, innovation, diversification, and entrepreneurship) were examined. An interesting result was that diversification was the most common word in each country along rural development programmes, while innovation was the most common word in documents linked to structural funds. A comment on the result is that these national documents were prepared following the EU regulations. Although the rural development regulations give a 'menu' for the countries and the national documents have to be prepared on the basis of the country's resources they are following the main lines coming from the regulations. Diversification is the most commonly used from the examined words in the EU regulation as well (Council regulation No. 1698/2005). The question is if diversification is an answer for the new member states, starting from a different basis.
Targeting region and people
The efficiency and targeting of measures of the EU rural development policy, the second pillar of the Common agricultural policy (CAP) has come to the front along the entry of Central and eastern European countries (CEEC) into the EU. Predominantly rural territories give a higher percent of member states' territory joined to the EU after 2004.
Copus et al. (2007) call the attention that 'some labour market themes/indicators already provide some evidence of systematic differentiation between rural regions of the EU'. Examining these indicators (patterns of demographic change; economic activity, employment and unemployment; sectoral/structural patterns and disparities in human capital endowment) they set up two groups 'accumulation' group, and 'depletion' group. A question again arises if rural development policy of the EU has to be targeted to the 'depletion' group.
Another possibility regarding targeting rural development funds is the Leader programme of the EU. The evaluation studies of LEADER II and the mid-term evaluation of LEADER+ suggest that such initiatives have a considerable impact on the development of rural regions, although their budget is small compared to mainstream programme instruments.
Governance
Results along the analysis of governance in the examined countries, we agree with the statement by Pop (2008), that although there are improvements in the new programming period between 2007-2013 in the new member states regarding the question of regional governance, 'there is still a limited interest in the new member states to develop extensive regional governance structures by creating new autonomous sub-national governance structures, which led to a weak institutionalisation of meso-level governments when compared to the institutional and policy structures within the EU-15'. Availability of time and capacity is also very important along governance.
We can conclude that each of the countries/regions are implementing the full range of European Strategies, policies, programmes (SPPs) available to them. It underlines the importance of well-developed SPPs. We can state that there are a considerable number and great diversity of national and regional policies with an impact on rural employment. The SPPs analysed are the most relevant ones, but we have to acknowledge that there are other policy documents (related to taxation, education, transport etc.) with an impact on rural employment.
Task 2.2 - Review and assessment of national and EU-funded research and dissemination projects (task leader: CRL; participants: all partners)
The deliverables are a synthesis report and database of relevant EU-level research and dissemination activities (D2.2) and a network of rural development academics and practitioners (D2.3). An extra deliverable 'The rural labour market' (D2.4) was composed to pull together our understanding of what is actually meant by the rural labour market.
D2.2
In D2.2 94 projects (studies, researches, under the Sixth and Seventh Framework Programmes, or Interreg III) linked with rural employment have been identified and reviewed which consists of three types of information. Based on the analysis recommendations for public authorities to act in favour of rural employment are developed.
The first part of the work dealt with methodological issues and ways to choose, to collect and to process and analyse data. Gathering of people from various disciplines of search, from various institutions and/or from various territories is recognised as a relevant way of sharing and valuing experiences that can be more useful to further an issue than alone. Knowledge is accessible through various sources and literature references. The more multi-disciplinary and wide this review is, the more information you get, even if this can be completed by surveys. Surveys can be used as quantitative mean of apprehending an overall point of view, or as qualitative interviews for spotted key experts or/and series of professionals.
The second section delivers basic knowledge about rural employment in Europe, about the labour market in rural areas and about rural development policies to set up so as to improve employment. Rural areas are isolated zones where transport and communication services are far less developed than in urban areas. Moreover, the choice of available jobs or training opportunities is recognised as lower. However, rural landscapes seem to provide a better quality of life and lower living costs.
The third part of D2.2 gives example of operational actions that can be implemented in various contexts to enhance entrepreneurship based on local resources. A consensus seems to have arisen concerning the diffusion of knowledge which is recognised as a great way of keeping people competitive. This can be done through networks on a same area, or through clusters of enterprises dealing with the same area of production. Moreover, knowledge can spread through dissemination events, and at last, it can be advertised through various means. Moreover, some projects detailed the implementation of innovative ideas regarding rural employment (e.g. support of local entrepreneurs). Thus, entrepreneurship in SMEs seems to hold an important part in the development actions of rural areas, as it has been demonstrated that having a great web of SMEs at a local level has a positive impact on local dynamism. Focus has also been done for job seekers, their information and training. A surprisingly low number of job-providing activities have been presented in the selected 94 projects.
Finally a list of recommendations, five series of recommendation, for public authorities to act in favour of rural employment emerges from the analysis of the findings. The first one regards the stake of the diffusion of knowledge which intends to play a key role in rural dynamism. A second series of recommendations concerns efforts to be done in order to reduce the inhibiting factors (living conditions, accessibility, infrastructures and ICT) of rural development. The third series that have been extracted is all about support that can be provided (financial, material and technical) to enterprises existing or in progress. The fourth series of recommendations that were formulated in the projects concerns territorial specificities or endogenous resources that have to be valued. And the fifth series give direct recommendations on the rural development policies and how to thoughtfully take decisions.
It can be concluded that the selection of projects provided many information about rural areas and the employment issue. The emergence of new areas of concern was found such as social capital, urban-rural linkages, or residential economics. However, some issues seem to be missing and the coverage was generally rather confined to the stereotypes of rural employment such as agriculture and tourism, however, the cost-efficiency and the sustainability were barely touched on.
This review of projects gives more elements that justify our work in RURALJOBS. Indeed, complementary analysis needs to be done to sort all the endogenous factors that must be taken into account before implementing rural policies, and hardly nothing has yet been proposed about differential policies that would adequately fits to each specific situation of infra-scale territories; from a European to local view.
D2.3
In task 2.2 a network of rural development academics and practitioners were constructed to enhance the relationship between individuals and organisations to further cooperation. In this document names and availability of academics and practitioners of rural development are listed.
D2.4
The'Rural labour market' document is an extra deliverable (D2.4) of RURALJOBS that brings together the ideas which have been formulated within the consortium during WP2. The main results are assessed under three main statements: the potential for the creation of activities is highly dependent on the territorial characteristics, the potential worker's capital vary from a territory to another and the way the demand and the offer of work interrelates is not automatic.
Employment development should base on local features. Geographico-historical background is influencing the economic distribution and strategy of territories, accessibility to cheap ways of delivery and sufficient market areas, and proximity to urban areas and public services. Local dynamism, including existing economic activities, available research partnership, and social capital allow a virtuous circle of dynamism to emerge. And finally local policy regulation and taxation might be limiting the motivation for activity creation, contrarily to public support and subsidies.
WP3 - Assessment methodologies and indicators (WP leader: BBU)
WP3 was designed to select methodologies that can be used to collect, from the study areas, the necessary data to assess, on the basis of a recognised set of indicators, labour market, demographic and economic trends, the impact of employment creation measures and policies in the reference areas and top-down and bottom-up constraints on their effectiveness.
Task 3.1 - Methodologies for evaluation of the rural area labour market, supply and demand of human resources (task leader: BBU; participants: all core partners)
Task 3.1 determined the methodologies to be employed to collect data sets of statistical, spatial, time series, etc. nature and defined data quality standards (actuality, accuracy etc.). It builds on the work of D2.2 and the results are presented in D3.1. In the various research projects reviewed by D2.2 a multitude of theoretical approaches related to research and to the choice of methodology were followed. In the elaboration of the methodology for RURALJOBS a combination of positivist and interpretativist/constructivist approaches was taken into account. During this process of elaboration of the methodology several quantitative-qualitative (QQ) methods were analysed and chosen to be used during the different phases of the research, methods that have been used during the last decades in research projects that had similar topics and similar aims.
The main method proposed for task 5.1 'Pilot areas research' is case study. The case study approach is complex and includes a variety of other, quantitative and qualitative methods. In our methodological approach the case study report (which will be the pilot area report), will be based on secondary analysis of statistical data and relevant literature about the pilot area (reports, monographs), semi-structured / in-depth interviews with 20 key informants and structured interviews for recording information about the good practices identified in the pilot area (successful initiatives for employment creation). The quantitative and qualitative data will allow us to draw preliminary conclusions about the rural employment situation in the pilot areas.
The methodology for task 5.2 Strengths, weaknesses, opportunities, and threats (SWOT)and Strategic orientation (SOR) analyses for reference and pilot areas' was already foreseen by the project proposal: based on the results of the research carried out in task 5.1 SWOT analysis will be conducted, followed by strategic orientation round analysis to evaluate the employment development potential of the pilot areas. D3.1 can add very little to these: testing out the adequacy of the proposed methods through the literature review and identifying focus groups as the method to validate the results obtained.
The present deliverable proposes for task 5.3 'Scenarios for pilot areas' a combination of two methods. The logical framework approach is widely used in development projects, but according to our knowledge, it has not been used as a research tool so far. The first part of the analysis required by this approach will be carried out already in tasks 5.1 and 5.2. Within task 5.3 we build up in each pilot area, using the already existing information, a problem tree, an objective tree and a logframe, as parts of a possible strategy for employment growth. Scenario planning is a method for learning about the future by understanding the nature and impact of the most uncertain and important driving forces. Alternative scenarios will be developed and, at workshops gathering key experts, the strategies generated in the previous stage will be tested against each of the scenarios.
By drawing comparisons across reference areas, initiatives and programmes identified as being effective in a pilot area in one reference area could be considered for pilot areas with of a similar type in other countries. Hence good practice can be extrapolated across the EU taking into account local rural culture and practices.
Task 3.2 - Indicator analysis and calibration (Task leader: UOP; participants: all core partners)
D3.2 describes the work of RURALJOBS task 3.2 'Indicator analysis and calibration'. It builds on the work of WP2, particularly deliverables D2.1/b. and D2.4. It uses DPSIR (driving force, pressure, state, impact and response) model as a tool to show the link between 'driving forces' which affect employment and policy responses. This model has been widely used with environmentally oriented indicator sets but has not previously been successfully applied to rural employment indicators.
The driving forces (or 'needs'), which influence the demand for workers and the supply of the workforce, and which represent targets for policy can be 'endogenous' (human, social, financial, natural or physical capitals) or (neo-) 'exogenous' (investors, market, knowledge centres, government and cultural assets). They act on the labour market or employment ('state') through the 'pressures' of jobs (economic activities) and people (the labour force). In turn, the employment rate (jobs per person) and associated parameters influence the 'impact' (sustainable economic prosperity, the key objective of the EU SDS). 'Responses' can be policy responses or socio-economic responses including commuting, migration, business relocation, etc.
Indicators were chosen on the basis of a series of strategies and programmes relevant to employment in rural areas in the EU, mostly identified by D2.1/b. This approach, rather than attempting to develop new indicators, should ensure that the indicators are widely recognised and Specific, measurable, attainable, results-oriented and timed (SMART). Essentially, context indicators were used to define driving forces, pressures, state and impacts, while indicators developed for the 'intervention logic' of programmes were used to identify responses.
A set of 40 indicators was compiled. Of these, 14 independent, policy-relevant indicators were selected to illustrate the range of endogenous driving forces which can have an impact on employment in rural areas (data not shown). Of a further 14 indicators, four describe pressures, six describe state and four describe impacts (table 1). A further eight (data not shown) are supplementary indicators of state. Finally, four indicators of socio-economic responses are identified (proportion of long-distance commuters; net migration; business creation and development; and attractiveness of the area). No indicators of exogenous driving forces appear in the final set, for reasons explained in D3.2 and the response indicators were used to produce a list of 'intervention topics' for which WP5 will identify examples of 'operational good practice'.
It is recognised that indicators which may, for example, be available at NUTS3 level in the Eurostat database will not necessarily be available according to the same definition at a more local level (such as LAU2 and LAU1) in all EU Member States. Only for the 'core' set of 14 pressure, state and impact indicators will RURALJOBS partners be required to collect comparable quantitative data, in order to address the WP5 research questions 'Is there a rural employment problem in the pilot area and, if so, what form does it take'? The other indicators have been used to define the focus of the desk-based research, and the qualitative research described under D3.1 such as interviews and focus groups.
In conclusion, WP3 has worked out the proper tools adapted to the (human and economic) resources and scientific specificity of each country. The methodological toolkit elaborated here could also be used (and applied) in other European rural regions in the future for similar analyses.
WP4 - Typology for regions (task leader: LUA)
The objectives of WP4 are to:
- review existing typologies of rural areas;
-define the typologies of the reference areas being studied by RURALJOBS;
-choose the suitable criteria for different regions;
-perform cluster analysis that to group the different types of regions;
-visualise the results into maps;
-interpret the results and conclusions of WP5 in the broader context of rural typologies.
Task 4.1 - Review of typologies (task leader: LUA; participants: all core partners)
Rural areas are very varied in accordance with spatial, social and economic dimensions and they are not homogenous. For this reason many different typologies have been developed that to determine the basic characteristics of rural areas and support different development policies. Typology of rural may be developed and applied the following uses:
1) for scientific knowledge of rural areas;
2) to administrate the different development policies of rural areas in EU;
3) to implement region or other local development programmes;
4) endogenous rural development.
There are many different typologies of rural areas which were prepared to support rural and labour market development. They are different according to its purpose, methodological principles, methods and others characteristics. The main types of typologies were identified for review of existing typologies of rural areas.
Rural areas typologies were analysed according to the methodological principles:
- policy measures/analytic tool;
-mostly, typologies are designed to discover similarities in regional structures;
- spatial/ performance;
-conceptual/ empirical;
-broad/ narrow;
-macro or micro/input-output/cost-benefit analysis/multicriteria analysis;
already implemented/ can easily be expanded/ would be rather difficult to expand/ would be impossible to expand;
- effective/neutral/ineffective.
The classification of rural areas typologies according to the methods, tools and indicators:
-Binary/multicriterial: A definition of 'rural area' is usually binary (rural versus urban), however for rural areas characterised the multicarerial analysis is used. In this case the typology is often built by more than two categories.
-One/more variables: The differentiating characteristics also vary in the typologies. Often one or more variables are used that to identify the proper characteristic. For instance, the rurality of region can be defined by number of population in the region, by number of population and density, by more than two indicators as in the typology of rural areas in the CEE New Member States.
-Static/dynamic: Static typologies does not account for the element of time, while a dynamic typologies does. Dynamic typologies typically are represented with difference equations or differential equations.
- QQ assessment: The criteria used should not just relate to outputs or factual information, but should incorporate more qualitative assessments to allow for a true picture to emerge. For example, the emphasis within the Canadian research on institutional capacity allowed for the consequent typologies to consider current circumstances and potential future directions. Such an approach would allow for perceptions of place and community priorities (possibly as defined through community planning) to be built into the analysis.
-Disaggregate/aggregative approach:Widely two broad 'families' of methods are used in the rural areas typologies - disaggregative and aggregative approaches. The disaggregative, where the one indicator is viewed as a single large group at the outset, to be progressively split into groups according to pre-selected discriminatory criteria (for example OECD rural areas typology), and the aggregative approach, where the types of rural areas are formed by similar characteristics of territories.
-Disaggregative approaches are less commonly used because of few statistical procedures are available that to categorise the indicators. They are essentially deductive and hardly can be validated.
-Aggregative methodologies associate with factor analysis to reduce a large number of variables to a few key dimensions, and by cluster analysis, to group the cases (rural areas) according to their pattern of scores on these dimensions. This approach may be viewed as 'inductive', since the clusters are determined by mathematical procedures, and the operator has no direct control over the character of the types which emerge.
-Simple deduction/ Principal components analysis (PCA): Methods are highly varying, from simple deductive methods based on setting threshold values for types to multistage methods and PCA.
-Rural/ rural and urban: Usually, both various rural types and various urban types are used.
- NUTS1/NUTS2/NUTS3/LAU1/LAU2. Most of the typologies based on the national (NUTS1- 2) and the regional levels (NUTS3). We can find the typologies build on the lowest administrative unit (LAU1-2) like: 'Scottish executive urban rural classification 2003-2004', 'Typology of rural areas in Italy and its role in strategic programming', 'Rural area typology in Finland', and others. The choice of administrative units depends on the purpose of typology and the official data which is linked with NUTS levels.
-2 types/n+1 types. The number of distinguished types is at minimum two, but often more types are distinguished, up to nine.
-Single/EU-wide typologies. The typologies can be split to the two groups: those which are EU-wide, and those which covering single (or groups of) Member States.
According to the defined characteristics over 60 typologies, which could potentially be applied to rural areas in the EU, were reviewed. The analysis of the existing typologies let present the following conclusions:
1. The main purpose of rural areas typologies is to ensure the policies objectives for rural areas which are multi dimensions; however the typologies are built on one or two criteria.
2. Most of the typologies are built as a rural development policy development tool but the close linked to the policy objectives and the indicators, which reflect the extent of regions achievement of strategic goals, are missed.
3. The indicators of rurality, peripherity and remoteness are dominated in the reviewed typologies.
4. Most of typologies development and empirically tasted models are carried on independently.
5. The typologies mainly developed on official statistical data which is linked with NUTS2 or NUTS3 levels. The most usage sources of statistics were: EUROSTAT, Organisation for OECD database, 2001 census results and some national statistics, however limited for whole EU-27.
6. The extended version of the OECD typology which distinguishes between peripheral and accessible regions within the predominantly rural and significantly rural categories can be used for RURALJOBS project typology development.
Task 4.2 - Define the typology of the regions and pilot areas
The deliverable of this task (4.2) is the calibration of the demographic and socio-economic criteria will be used to select the most appropriate criteria and indexes for defining the typology of the pilot areas (probably LAU1 level or equivalent) that will be studied in WP5. Data from EUROSTAT, national and regional statistics, plans, regional investigation results will be used that prepare the regional types of labour market.
According to the conclusions of review of typology literature, there are a lot of indicators, indexes and criteria which can be used for analysis of regions and their employment system development. Each indicator or criteria has its own characteristics which define availability and suitability for certain analysis. One of the major characteristics is regional level chosen for the typology. For the purpose of the typology of EU-27 regions NUTS3 level was chosen. Regions of NUTS0 to NUTS2 category are too broad for the analysis of local employment system. The very smallest administrative units (LAU1 and LAU2) has relatively poor list of data available for all EU-27 countries. Also the number of the LAU1 or LAU2 regions significantly bigger than NUTS3 regions, thus it would take significantly more resources for analysis and effectiveness of such analysis is uncertain. There are 1303 NUTS3 regions in EU-27 and over 40 indicators available for the analysis on NUTS3 level. So NUTS3 level is an optimal for creation the regional typology for the RURALJOBS project.
A list of 21 indicators was generated for selection of the criteria and indicators which are most suitable for the project. Indicators are available on Eurostat database and from previous studies on rural and regional development. The indicators on the lists are:
1) population (thou);
2) population density (habitants / km2);
3) economically active population (in thousands);
4) share of economically active population of total population;
5) gross domestic product at current market prices;
6) GDP per capita;
7) GDP per capita % EU average;
8) GVA in agriculture and fishing;
9) GVA in industry;
10) GVA in services;
11) share of GVA in agriculture of total GVA;
12) share of GVA in industry of total GVA;
13) share of GVA in services of total GVA;
14) unemployment rate;
15) employment (in thou) in agriculture and fishing;
16) employment (in thou) in non-agricultural sectors;
17) share of employment in agriculture and fishing of total employment;
18) share of employment in non-agricultural sectors of total employment;
19) bed-places in hotel;
20) % of NUTS3 population living within 45 minutes driving time from centroids of cities with at least 50 thou inhabitants; and
21) rurality index.
Rurality index measures a share (%) of total population of the region living in the rural municipalities. The generated list of the indicators was analysed using PCA. According to the results of the PCA ten factors (called 'latent' factor) were excluded. Factor 1 'explains'GVA in industry by 91%, GVA in services by 86%, employment in non-agricultural sector by 97%, economically active population by 97%, population by 96% and GDP by 89%. That means there is strong causal-effects relationship among these indicators and they could be explained relatively very well by single 'latent' factor. The rest of the 'latent' factors explain much less correlations between the indicators.
Indicators that could be hardly explained by latent factor were selected for the typology generation process. The selection is based on presumption that such indicators have weak correlation among each other and combination of such indicators reveals more valuable information about the region. Those indicators are:
1) accessibility;
2) unemployment;
3) rurality index;
4) GDP per capita (or GDP per capita compared to EU average);
and 5) bed places in hotels and similar places.
Unemployment depends on short and long term changes such as seasonal, frictional and structural unemployment. Short term factors has significant share in total numbers, thus unemployment is rather short term indicator than long-term. Typology has to be based on long-term (or so called structural) indicators thus unemployment is excluded from the further analysis.
GDP per capital and bed place in hotels indicate the level of economic activity there, but the former is more aggregated one while the latter is more specific. GDP per capita better fits for the objectives of the typology for this project. And as there is no difference in using GDP per capita or GDP per capital as compared to EU average, the latter is used because it is more convenient for the grouping of the regions.
Based on the literature review and statistical analysis a conclusion was made that the typology of NUTS3 regions in EU-27 shall be developed by using the rurality index, accessibility, and GDP per capital as compared to EU average criteria.
Rurality index is useful for the grouping regions as rural or urban. The indicator is not common in the studies and projects on the Rural and Agriculture development issues and in typologies. Cook and Mizer used an indicator of population number in towns of more than 2500 population.
Rurality index was developed by OECD. Share of population living in the rural municipalities reflect better picture of population distribution compared to population density. Densely populated regions could be rural in terms of geographical scattered living area. Also low population density regions could be urban because of a population living in cities which are surrounded by large territory of wild nature.
Accessibility (i.e. access to city, in minutes) reveals the possibility for the local labour force to get job in the neighbourhood areas. Dijkstra and Poelman used three benchmarks for this indicator, namely 1) 30 min., 2) 45 min, and 3) 60 min. of driving time by car to the city of more than 50 thou habitants. Based on the data collected from previous studies the Rural Jobs project uses 45 min benchmark. The results revealed the tendency that the better develop road and railway network in the region, the more developed region is.
GDP per capita, % of EU average indicates how different development of economic system and living conditions are among the regions. It is a structural indicator which does not change rapidly in short-term and thus is useful for generating typologies. For the RURALJOBS project objectives there are two groups of regions divided by this indicator: 1) 'developed countries' includes regions with GDP per capital 50% of EU average and less, and 2) 'developing countries' includes the regions with GDP per capital 50% of EU-27 average and more. According to the results the GDP per capital divided EU-27 regions into two majors groups, i.e. 'Western and northern Europe' and 'Central and eastern Europe'. In other words the indicator explicitly divided New Member States and Old Member States.
The whole set of these three indicators generates typology which allow to group the regions according to the characteristics of the area, i.e.:
1) how much population lives in rural areas;
2) how the regions are well developed in economic sense;
and 3) how large 'labour market territory' is for the local habitants. Using selected benchmarks of each indicator 12 types of regions were calculated.
Because of limited number of reference areas a modification was made, that is number of types were reduced from 12 to 7. The seven types are:
1) predominantly rural - remote - developing region (3% of total number of NUTS3 regions);
2) predominantly rural - remote - developed region (8 %);
3) predominantly rural - accessible - developing region (6 %);
4) predominantly rural - accessible - developed region (15%);
5) significantly rural - developing region (6%);
6) significantly rural - developed region (29%), and
7) predominantly urban (32%).
The types of EU-27 regions were visualised into the Geographic information system (GIS) maps.
Task 4.3 - Regional foresight scenarios
The main objectives of the task 4.3 were to:
(a) review the methodologies for forecast scenarios building;
(b) propose the methodology based on demographic, economic and labour market data available at Eurostat data base to compute regional forecasts;
(c) prepare the forecast scenarios according to RURALJOBStypology for EU-27 regions at NUTS3 level.
The new methodology to compute labour market forecasts for EU-27 at regional level have been proposed by LUA researches. Autoregressive integrated moving average (ARIMA), Exponentially weighted moving averages (EWMA) and structural-component (SC) estimators methods were integrated into the purposed forecasts model. The outcome of the labour market was estimated by the value of a GVA in the following five sectors according to NACE classification:
1) agriculture, hunting, forestry and fishing (AB);
2) industry and constructions (CF);
3) wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods; hotels and restaurants; transport, storage and communication (GHI);
4) financial intermediation; real estate, renting and business activities (JK); and
4) public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons (LP).
The data to compute regional forecasts was used from the period 1995-2007 and forecast period 2008-2020.
The empirical application of short-term forecasts for employment in 1303 EU regions provide the following results (findings):
Type 1 - Predominantly rural/ Remote/ Developing - 39 EU regions (3%).
Share of employment in agriculture and fishing is expected to decrease to quarter of the total employment in type 1 regions. The decline of the share shall follow the steady pace of 2.5 percentage points on average per seven-year period. The biggest beneficiary of the 'loss' of the agriculture shall be sector of wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods, hotels and restaurants, transport, storage and communication (GHI). During the period of 2010-2020 the share of GHI sector shall increase by 1.7 percentage points to 21.3. It is expected to be the fastest growth in type 1 regions.
Type 2 - Predominantly rural /Remote/Developed - 109 EU regions (8%).
The average share of employment in agriculture and fishing in type 2 regions are twice smaller compared to the type 1 regions. This sector is expected to face further strong decline by 2.2 percentage points in 2010-2020. Continuing technological innovations is expected to force farmers to increase the efficiency of a labour force which is scarce in remote regions. The externality of such innovations is a decreasing number of jobs available in agriculture and fishing which one of the most important economic activities for rural viability. Public administration and defense (LP sector) sector would rally in terms of employment creation in type 2 regions during 2010-2020. The share of LP sectors is expected to increase to 32.8 % in 2020. Rapid developments of public sector in the type 2 regions would generate faster growth for financial intermediation, real estate and other business activities compared to other sectors.
Type 3 - Predominantly rural /Accessible/Developing - 77 EU regions (6 %).
Share of employment in agriculture and fisheries is expected to decrease by 2.5 percentage points from 25.8 % in 2010 to 23.3 % in 2020. The loss of jobs in agriculture sector would be compensated by the new jobs in transport, retail, communications and public administration sectors. Share of the employment in transport, retail and communication sector would grow by 1.5 percentage points while in public administration by 1.4 percentage points during 2010-2020. Predominantly rural areas in less developed regions face convergence effect when changes in structure are more volatile. On the other hand developing regions tend to move new jobs creation process to the transport, retail, communication sectors and public administration. Having in mind the availability of human, social and physical infrastructure in type 3 regions, it is hard to expect rapid breakthrough in industry of business services.
Type 4 - Predominantly rural /Accessible/Developed - 198 EU regions (15 %).
Most volatile changes in employment structure are expected in type 4 regions. Rural development policy has a strong impact on changes in employment structure there. Relative less number of jobs is expected to be available in agriculture and industry and relatively more number of jobs is expected in services. During 2010-2020 the share of employment in industry would decrease by 3.6 percentage points to the 25.4 %, the share of employment in agriculture and fisheries down by 2.0 percentage points to 5.7 %. Instead the share of employment in transport, retail and communications is expected to increase by 2.1 percentage points to 25.5 % and will overcome the number of jobs in industry. Similar growth is expected for public administration employment. In 2020 it is estimated to have almost 1/3 of jobs in type 4 regions. Employment in financial and other business services is expected to increase to 10.8 % in 2020. Thus critical role for employment in accessible rural areas with high GDP per capita would play services.
Type 5 - Significantly rural /Developing - 90 EU regions (7%).
Less developed EU-27 regions were going through rapid structural changes in term of employment but relatively less rapid structural changes in terms of GVA. It is expected that 1.5 percentage point decrease in share of employment in agriculture in 2010-2020 to be compensated by the 1.5 percentage point growth in share of employment in retail, transport and communication sector. Loss of the share in industry would be substituted by 0.5 percentage point growth in financial and retail services and by 0.2 percentage points growth in public administration. Relatively modest growth of employment in public administration leads to a conclusion that institutional developments are well integrated into EU market and policy, and that further advancements there are in terms of innovation of the most recent technologies and policies.
Type 6 - Significantly rural /Developed - 377 EU regions (29%).
Compared to type 5 regions, type 6 regions are trended to much more radical institutional changes in public administration. In 2010-2020 it is expected that the share of employment in public administration would increase to 1/3 of all employment in these regions, indicating the importance of state and performance public finances and administration there. Relatively rapid growth in creating jobs will indicate other services (transport, retailers, financial intermediation, real estate and etc.). The biggest loser in terms of the changes in share of employment in type 6 regions is expected to be industry with 3.6 percentage decrease during 2010-2020. Share of employment in agriculture and fisheries will decrease by 1.1 percentage points to the 3.9 % on average in type 6 regions.
Type 7 - Urban regions - 413 EU regions (32%).
Almost one third of all EU-27 NUTS3 regions, are expected to have an average growth of employment in agriculture and fisheries while losses in employment in industry would be compensated by growth in public administration and business consultations. In 2014-2020 it is expected the share of employment in agriculture to decrease insignificantly while decrease in employment in industry would face 2.6 percentage points decline to 20.9 % in 2020. The share of service since 2014 will increase by 3.0 percentage points to 78.0 % in 2020. Rapid increase of employment in services is in line with Europe 2020 strategy to become smarter and more inclusive economy.
Conclusions
Typology of the EU-27 regions is based on complex multi-criteria analysis based on the demographic, economic and territorial criteria which are backed by the data available for NUTS3 level.
Optimal level for the typology of EU-27 regions is NUTS3 level. There sufficiently large number (i.e. 1303) of NUTS3 regions in EU-27 for the representation of socio-economic and demographic situation in the region. Also there are over 40 indicators available for NUTS3 regions on Eurostat and other databases. That ensures detailed and systemic approach for the process of grouping EU-27 regions into certain types.
According to the proposed regional foresight it is expected significant changes in employment structure in rural areas across the EU-27 during the period 2010-2020. Six types of rural areas will face different trends of the changes of the structure, but they have one common feature in common, i.e. rapid decrease of employment in agriculture and fisheries. Thus ongoing process of relaxing labour force from agricultural activities generates economic, social and political stress. The threat of increasing unemployment and degradation of human resources in rural areas shall encourage better focus and better targeting of Rural Development policy measures.
As the estimations of the model revealed attractiveness of the local area would play critical role in developmental processes of the regions. One of the most important factors of the attractiveness of the local area is quality of life indicated by GDP per capita level compared to the EU-27. The higher the quality of life a region has the better business demographic dynamics there.
Calculations also revealed a threat of divergent process among urban and rural areas. The level of GDP per capita in urban areas is expected to stay well above the average of the EU-27, while the predominantly rural areas in developing countries will stay at 30 % of the EU-27 average and with more than 2.2 loss of population during 2010-2020.
WP5 - New strategies for employment in pilot areas (WP leader: UoP)
The objectives of WP5 were to identify labour market, demographic and economic trends in a selection of representative pilot areas and to identify employment growth areas in the context of available human capital, skills and adaptability as well as demand for labour and existence of top-down and bottom-up constraints.
The methodology formulated in WP3 will be applied in WP5.
Task 5.1 - Pilot areas research (task leader: UOP; participants: all core partners)
At the time of writing, the RURALJOBS partners participating in task 5.1. were in the process of finalising their pilot areas and arranging interviews with representatives of the following groups:
(a) elected decision makers;
(b) local government experts;
(c) other experts such as academics and consultants;
(d) community organisations and NGOs including churches; and
(e) the business sector, including chambers of commerce and farmers' unions.
Approximately four representatives from each group will be interviewed, i.e. around 20 interviewees in total. Partners were also starting to identify sources of data, preferably official data sets at LAU2 level, for the fourteen indicators listed in table 1. No results are yet available from task 5.1.
Tasks 5.1 - Pilot areas research and 5.2 - SWOT and SOR analyses for pilot areas
The output of these two tasks was combined into one deliverable, D5.1. (Individual reference area reports on current employment patterns and opportunities for, and constraints on, rural economic diversification and identifying examples of good practice).
TheRURALJOBS review of previous relevant research noted many different approaches to defining the boundaries of study areas for field research. Frequently, administrative boundaries (NUTS2, NUTS3 or LAU1) were used. As our research was expected to 'examine the interaction between different types of rural area (peri-urban, remote, high environmental/amenity value etc.) and the evolution of labour markets, travel to work areas and changing work patterns', we opted to use 'labour market' or 'employment' areas. Remarkably, in most countries represented in the RURALJOBS research, evidence was available which allowed these areas to be defined, as follows: 'Travel to work areas' (TTWA) in the UK; 'Local labour systems' (LLS) in Hungary; and 'agglomeration areas' in Bulgaria. In France, a 'Pays' is the result of a collective bottom-up approach with regional approval of its boundary. Only in Romania was it necessary to use an administrative territory (a NUTS3 region) as a pilot area.
TheRURALJOBS project started on 1 February 2008, and the project duration was 33 months.
The research objectives set out in the call for proposals were that the project will identify labour market, demographic and economic trends in rural areas across European Union (EU)-27 and the potential for new sources of employment outside traditional primary and secondary sector activities. It will take into account the European guidelines for employment, technological change and the shift to a knowledge based economy. It will focus on human capital, skills and adaptability, as well as demand for labour in sectors such as the provision of environmental services, recreational amenities and traditional skills. It will examine the interaction between different types of rural area (peri-urban, remote, high environmental/amenity value etc.) and the evolution of labour markets, travel to work areas and changing work patterns. It will identify employment growth areas where rural development programmes can be targeted to increase their contribution to employment creation. The expected impact was defined as follows: 'The project results will allow a better targeting of rural development measures and future evolution of rural development policies in line with the Lisbon Strategy'.
In response to the call, the aim of RURALJOBS was to quantify the employment needs and potentials in different types of pilot areas within contrasting reference areas in seven EU countries, evaluating the effectiveness of past and current policies in addressing these needs and potentials, and by systematic analysis of the results, providing guidelines on the better targeting of future rural development measures. The main objective was to provide a clearer understanding of the factors influencing the employment potentials of different types of rural areas to support the future evolution of rural development policies. This was backed up by the identification of good practice and a support network for implementation.
RURALJOBS implemented the planned tasks and fully achieved its objectives. The project was carried out in seven Work packages (WPs).
WP1 and WP7 started at the beginning of the project and continued throughout the life of the project ensuring the overall project management (WP1) and the dissemination of the project (WP7). Most of the work was carried out in WPs 2-4 during the first reporting period, and the implementation of the tasks in WP5 and WP6 was scheduled in the second period.
The project website was regularly updated during the project period.
Project context and objectives:
RURALJOBS worked on quantifying the employment needs and potentials in different typologies of pilot areas within contrasting reference areas in seven countries, evaluating the effectiveness of past and current policies in addressing these needs and potentials, and by systematic analysis of the results, providing guidelines on the better targeting of future rural development measures.
The main objective was to provide a clearer understanding of the factors influencing the employment potentials of different typologies of rural areas to support the future evolution ofrural development policies. This was backed up by the identification of good practice and a support network for implementation.
RURALJOBS addressed the following community strategic guidelines for rural development:
-improving the competitiveness of the agricultural and forestry sectors;
-improving the quality of life in rural areas and encouraging diversification;
- building local capacity for employment and diversification;
-translating priorities into programmes;
-developing complementarity between Community Instruments.
RURALJOBS had four strategic objectives:
(a) review of employment policies and programmes;
(b) scenarios for new sources of employment according to rural typologies;
(c) recommendations for better targeting of strategies; and
(d) dissemination and mainstreaming.
The consortium consisted of eight partners, one from each of Bulgaria, France, Hungary, Italy, Lithuania, Romania, Spain and United Kingdom.
The seven WPs were:
WP1 - Project management
WP2 - Assessment of labour market policies and programmes
WP3 - Assessment methodologies and indicators
WP4 - Typology for regions
WP5 - New strategies for employment in pilot areas
WP6 - Synthesis of recommendations
WP7 - Dissemination and technical assistance for mainstreaming
The beneficiaries included policy makers at EU, national and regional levels, rural development practitioners including public sector agencies, Small and medium-sized enterprises (SMEs) and trade groups, Non-governmental organisations (NGOs) and academics. The deliverables are: reports on past and current policies and programmes,assessment methodologies, rural typologies, labour market scenarios for pilot areas and regional foresight scenarios; a synthesis of recommendations for future rural development strategies, four regional conferences/workshops; a two-day international conference in Brussels; academic publications; training materials and a up to date website.
The sustainable outcome of RURALJOBS is the greater capacity of actors to better target rural development measures, supported by the RUR@CT network and reference area reports on mainstreaming good practice.
Project results:
WP2 - Assessment of labour market policies and programmes (WP leader: DE)
Task 2.1 - Review of past and current labour market strategies and programmes in the EU (Task leader: DE; participants: all partners)
Deliverable 2.1
Task 2.1 first part
The main findings from the statistical analysis of European rural regions are composed in four parts: specific issues potentially affecting employment rates, comparison and the status of urban and rural regions, differences between EU-15 and post-socialist Non-Member States and results of national-regional reports of RURALJOBS partners. Analysis was performed on NUTS2 and NUTS3 levels based on the general and regional statistics of Eurostat dividing the regions in three categories: predominantly rural, intermediately rural and predominantly urban.
Specific issues potentially affecting employment rates consists of five components: demographic development, employment-unemployment, economic progress of rural areas, education, and Human resources in science and technology and research and development.
In terms of demography the EU needs to solve the problem of negative trends in population and labour force aging. The tendency of natural population change increased the disadvantageous position of rural areas. On the EU level the natural change of the population was negative but the average number of population increased moderately from 2000 to 2006. The source of the increase of the population was the net migration to the EU. One way of solving the problem of decreasing population is to increase the immigration however according to numerous studies the negative tendency cannot be compensated by reasonable immigration.
The urbanisation process continued and the gap in population density increased between PR and PU regions and between PR and IR regions from 2000 to 2006. The ratio of females became more significant mainly because the longer life expectancy of women. The number of females was higher than the number of males in each region type and the difference between the female and male annual average population increased in PU, IR and PR regions from 2000 to 2006.
Analysing the employment and unemployment tendencies significant differences were found in urban and rural areas. While the development of employment was the most intensive in IR regions negative tendencies were experienced in PR regions. Total employment generally increased in PU (4.12%), IR (4.25%) and decreased in PR (-0.74%) regions in the EU from 2000 to 2005.
European regions face the consequences of rapid and unequal development of the service sector. It was found that the ratio of people employed in services PU or IR regions was significantly higher than it was in PR region that suggests that rural people's access to various services is very limited in comparison with the possibilities of inhabitants in PU and IR areas which is an important disadvantage of the rural life. The importance of services accelerated in rural areas.
Employment in agriculture, hunting, forestry and fishing decreased greatly in PU, IR and PU regions of the EU the most significant decline of 16.09% happened in PR regions. Regions with small farm holdings generally correlated with low productivity that decreases the competitiveness of these businesses. The share of employment in industry also decreased.
Employment pattern is changing through age groups and gender. Employment rates of youth and elderly are lower than the employment rate of prime-aged people. Employment rate of prime-aged women is generally lower than employment rate of prime-aged men. Youth unemployment rates are generally higher in every region than prime-age unemployment rates therefore a notable part of the potential of the most active group of the workforce is not utilised.
The structure of employment was different in urban and rural areas of the EU. The ratio of employees was the highest in PU regions and the lowest in PR regions in 2000 and also in 2006. The ratio of self-employed people and family workers showed an opposite tendency with low ratios in urban and higher ratios in rural areas. The rate of family workers was about three times more in IR and PR regions then in PU regions.
The unemployment gap increased between rural and urban areas since the difference in unemployment rates between developed regions and less developed regions have increased, getting lower in developed regions and increasing in less developed regions. Long-term unemployment rate was slightly lower in PR regions (37.66%) than in PU and IR regions (2006).
Economic development was more intense in urban areas than in rural regions since the Gross domestic product (GDP) euro per inhabitant in percentage of the EU average was the highest in PU regions (129.2%), less in IR (84.4%) and the lowest in PR (76.4%) regions (2005). Despite the big differences in GDP between the region-types at the beginning of the period the highest development was realised in the most developed PU areas and the lowest increase happened in PR regions which increased the gap between the urban and rural areas.
Quality in higher education is becoming an essential aspect of education as the number of students increased steeply in many European countries. Demographic trends and rapid technological evolution will require shifts in the structure of employment and more people will work in the education system partly to serve the needs of lifelong learning. Transformative and empowering learning can serve the best interest of modern enterprises therefore higher education has to be transformed to this direction. This transformation can be achieved mainly through the extensive application of lifelong learning in higher education.
The ratio of human resources in science and technology was the lowest in rural areas. The development of human resources in science and technology was rather intensive. The increase of human resources in science and technology was 20.28% in PR regions and 16.15% and 15.63% in PU and IR regions.
Comparison and the status of urban and rural regions illustrate the relation of these region types. Comparison of the three region types based on the selected indexes. It was found that in the majority of cases PU regions differs greatly from IR and PR regions than IR regions differ from PR regions. The following examined indexes can mainly prove the above mentioned differences of PU, IR and PR regions:
-the average and total area;
-natural population change;
-population density;
-GDP per inhabitant at current market prices;
- Gross value added (GVA) at basic prices;
-the share of agriculture, hunting, forestry and fishing, industry and services in GVA;
-income of households;
-employment by economic activity;
-employment by professional status, the division of employees, self-employed and family workers;
-unemployment rates of inhabitants, age of 25 years and over;
-human resources in science and technology as the percentage of total population;
-patent applications to the EPO by priority year per million of inhabitants.
The changes in index values from 2000 to 2006 were not always consistent with the above tendencies the increase or decrease of values were not associated with the region type. Moreover, changes of values did not affect generally the relative positions of PU, IR and PR regions for seven years.
In many cases there were found considerable differences between EU-15 and post-socialist Non-Member States. As a result of the migration and natural population change the population density of former socialist countries (Bulgaria, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia and Slovakia) decreased in PR regions and it decreased also in the majority of PU and IR regions. In other countries the population density increased in all region types except in PR regions of Denmark, Greece, and Portugal and in every category in Denmark.
In eastern European countries' total employment increased in PU regions end decreased in PR regions which suggest that the migration of employees from rural areas to urban areas was a characteristic feature of these countries. In former communist countries, except Poland, PU regions had much more higher rate of human resources in science and technology than IR and PR regions mainly as a consequence of the centralised economy, they inherited. The number of patent applications per million of inhabitants to the EPO by priority year was the highest in EU-15 countries with some exceptions far ahead of the Non-Member States.
The status and tendencies of rural employment and development were analysed by each partner on country and/or NUTS 2 levels. Six partners has reference regions on NUTS 2 level (DE, UOP, BBU, LUA, and AND, CRL), two partners (IAE and INIPA) reported on country level. Agriculture has a diverse but diminishing importance in RURALJOBS' countries and reference regions; however the small average farm size is a general problem which is a barrier to efficient agricultural production. Overall the average farm size increased in the period of 2000 to 2006. In the four EU-15 countries the diversification of the farm activities created new sources of employment for the rural population. Mainly in these countries organic farming went through an evolution and became a profitable business in many regions. In Bulgaria, Hungary and Romania the outworn facilities, out of date technologies and inefficient integration are common reasons of inefficiency of agricultural production.
At large growing employment characterised the labour market of the RURALJOBS countries and the reference regions but the employment rates were below 70% except in Essex. In countries where the net migration was negative emigration was a notable factor of unemployment reduction. The unemployment rates were higher in rural regions with one exception.
Summarising the findings from the statistics it can be stated that despite the negative tendencies in demography the EU has gone through a development in many areas. Improvements were experienced in the employment, income of countries, and higher education in both rural and urban areas although the development was more intensive in urban areas which resulted in an increasing gap in favour of urban regions. This phenomenon makes the problems of rural development and rural employment relatively more important than it was a decade ago.
Task 2.1 second part
Employment is a cross cutting principle in EU policies, not targeted separately to rural areas, although lot of measures along different policies address rural areas and have an effect on rural employment. On the other hand 'mainstreaming' employment policy is an important principle. Article 147 (e.x.article 127) 'The objective of a high level of employment shall be taken into consideration in the formulation and implementation of Union policies and activities' - requires that the employment impact of all community policies must be taken into account, including rural development policy. Member States have the main responsibility for employment and social policy. The EU's role is to be a catalyst for change and modernisation. It supports, accompanies and coordinates government efforts to reshape their employment and social policies.
Sustainable development as a framework
In our understanding it is also important to create a structure between the strategies. Following the changes along globalisation, the results of the Word Summit in Johannesburg in 1992 and also the renewed Sustainable development strategy (SDS) of the EU in which it is stated that 'the EU SDS forms the overall framework' SDS of the EU was taken as the one above all the others in our work. It is also important to find the links between the created strategies. The structure of the strategies and policies examined in our work is drawn up in Figure 1. Employment strategy forms a part of Lisbon Strategy from 2000 and both of these strategies have to be placed under the strategy of sustainable development.
While EU gives guidelines (often in the form of regulation, or using open method of coordination) for development strategies next to different policies, there is not any guideline in the case of SDSs. Also the timeframe of the member states SDSs and the year of acceptance differ between the countries. SDSs of EU countries do not have separate EU funds.
Long-term economic, social and environmental indicators give a better picture of a country's progress than do traditional methods. Measured in sustainable development terms the EU-27 leads the word. This is also underlined by the fact that ten years after Rio, from the about 6 500 authorities, which begun their own local agenda 21 processes more than 5000 are in Europe. These issues from the dimension of sustainability create new possibilities for rural employment. It also has to be taken into consideration that human resources are renewable resources of the economy.
Harmonisation of regional and rural development policies
Along the analysis of the documents the turnout of selected words related to employment (adaptability, knowledge-based, innovation, diversification, and entrepreneurship) were examined. An interesting result was that diversification was the most common word in each country along rural development programmes, while innovation was the most common word in documents linked to structural funds. A comment on the result is that these national documents were prepared following the EU regulations. Although the rural development regulations give a 'menu' for the countries and the national documents have to be prepared on the basis of the country's resources they are following the main lines coming from the regulations. Diversification is the most commonly used from the examined words in the EU regulation as well (Council regulation No. 1698/2005). The question is if diversification is an answer for the new member states, starting from a different basis.
Targeting region and people
The efficiency and targeting of measures of the EU rural development policy, the second pillar of the Common agricultural policy (CAP) has come to the front along the entry of Central and eastern European countries (CEEC) into the EU. Predominantly rural territories give a higher percent of member states' territory joined to the EU after 2004.
Copus et al. (2007) call the attention that 'some labour market themes/indicators already provide some evidence of systematic differentiation between rural regions of the EU'. Examining these indicators (patterns of demographic change; economic activity, employment and unemployment; sectoral/structural patterns and disparities in human capital endowment) they set up two groups 'accumulation' group, and 'depletion' group. A question again arises if rural development policy of the EU has to be targeted to the 'depletion' group.
Another possibility regarding targeting rural development funds is the Leader programme of the EU. The evaluation studies of LEADER II and the mid-term evaluation of LEADER+ suggest that such initiatives have a considerable impact on the development of rural regions, although their budget is small compared to mainstream programme instruments.
Governance
Results along the analysis of governance in the examined countries, we agree with the statement by Pop (2008), that although there are improvements in the new programming period between 2007-2013 in the new member states regarding the question of regional governance, 'there is still a limited interest in the new member states to develop extensive regional governance structures by creating new autonomous sub-national governance structures, which led to a weak institutionalisation of meso-level governments when compared to the institutional and policy structures within the EU-15'. Availability of time and capacity is also very important along governance.
We can conclude that each of the countries/regions are implementing the full range of European Strategies, policies, programmes (SPPs) available to them. It underlines the importance of well-developed SPPs. We can state that there are a considerable number and great diversity of national and regional policies with an impact on rural employment. The SPPs analysed are the most relevant ones, but we have to acknowledge that there are other policy documents (related to taxation, education, transport etc.) with an impact on rural employment.
Task 2.2 - Review and assessment of national and EU-funded research and dissemination projects (task leader: CRL; participants: all partners)
The deliverables are a synthesis report and database of relevant EU-level research and dissemination activities (D2.2) and a network of rural development academics and practitioners (D2.3). An extra deliverable 'The rural labour market' (D2.4) was composed to pull together our understanding of what is actually meant by the rural labour market.
D2.2
In D2.2 94 projects (studies, researches, under the Sixth and Seventh Framework Programmes, or Interreg III) linked with rural employment have been identified and reviewed which consists of three types of information. Based on the analysis recommendations for public authorities to act in favour of rural employment are developed.
The first part of the work dealt with methodological issues and ways to choose, to collect and to process and analyse data. Gathering of people from various disciplines of search, from various institutions and/or from various territories is recognised as a relevant way of sharing and valuing experiences that can be more useful to further an issue than alone. Knowledge is accessible through various sources and literature references. The more multi-disciplinary and wide this review is, the more information you get, even if this can be completed by surveys. Surveys can be used as quantitative mean of apprehending an overall point of view, or as qualitative interviews for spotted key experts or/and series of professionals.
The second section delivers basic knowledge about rural employment in Europe, about the labour market in rural areas and about rural development policies to set up so as to improve employment. Rural areas are isolated zones where transport and communication services are far less developed than in urban areas. Moreover, the choice of available jobs or training opportunities is recognised as lower. However, rural landscapes seem to provide a better quality of life and lower living costs.
The third part of D2.2 gives example of operational actions that can be implemented in various contexts to enhance entrepreneurship based on local resources. A consensus seems to have arisen concerning the diffusion of knowledge which is recognised as a great way of keeping people competitive. This can be done through networks on a same area, or through clusters of enterprises dealing with the same area of production. Moreover, knowledge can spread through dissemination events, and at last, it can be advertised through various means. Moreover, some projects detailed the implementation of innovative ideas regarding rural employment (e.g. support of local entrepreneurs). Thus, entrepreneurship in SMEs seems to hold an important part in the development actions of rural areas, as it has been demonstrated that having a great web of SMEs at a local level has a positive impact on local dynamism. Focus has also been done for job seekers, their information and training. A surprisingly low number of job-providing activities have been presented in the selected 94 projects.
Finally a list of recommendations, five series of recommendation, for public authorities to act in favour of rural employment emerges from the analysis of the findings. The first one regards the stake of the diffusion of knowledge which intends to play a key role in rural dynamism. A second series of recommendations concerns efforts to be done in order to reduce the inhibiting factors (living conditions, accessibility, infrastructures and ICT) of rural development. The third series that have been extracted is all about support that can be provided (financial, material and technical) to enterprises existing or in progress. The fourth series of recommendations that were formulated in the projects concerns territorial specificities or endogenous resources that have to be valued. And the fifth series give direct recommendations on the rural development policies and how to thoughtfully take decisions.
It can be concluded that the selection of projects provided many information about rural areas and the employment issue. The emergence of new areas of concern was found such as social capital, urban-rural linkages, or residential economics. However, some issues seem to be missing and the coverage was generally rather confined to the stereotypes of rural employment such as agriculture and tourism, however, the cost-efficiency and the sustainability were barely touched on.
This review of projects gives more elements that justify our work in RURALJOBS. Indeed, complementary analysis needs to be done to sort all the endogenous factors that must be taken into account before implementing rural policies, and hardly nothing has yet been proposed about differential policies that would adequately fits to each specific situation of infra-scale territories; from a European to local view.
D2.3
In task 2.2 a network of rural development academics and practitioners were constructed to enhance the relationship between individuals and organisations to further cooperation. In this document names and availability of academics and practitioners of rural development are listed.
D2.4
The'Rural labour market' document is an extra deliverable (D2.4) of RURALJOBS that brings together the ideas which have been formulated within the consortium during WP2. The main results are assessed under three main statements: the potential for the creation of activities is highly dependent on the territorial characteristics, the potential worker's capital vary from a territory to another and the way the demand and the offer of work interrelates is not automatic.
Employment development should base on local features. Geographico-historical background is influencing the economic distribution and strategy of territories, accessibility to cheap ways of delivery and sufficient market areas, and proximity to urban areas and public services. Local dynamism, including existing economic activities, available research partnership, and social capital allow a virtuous circle of dynamism to emerge. And finally local policy regulation and taxation might be limiting the motivation for activity creation, contrarily to public support and subsidies.
WP3 - Assessment methodologies and indicators (WP leader: BBU)
WP3 was designed to select methodologies that can be used to collect, from the study areas, the necessary data to assess, on the basis of a recognised set of indicators, labour market, demographic and economic trends, the impact of employment creation measures and policies in the reference areas and top-down and bottom-up constraints on their effectiveness.
Task 3.1 - Methodologies for evaluation of the rural area labour market, supply and demand of human resources (task leader: BBU; participants: all core partners)
Task 3.1 determined the methodologies to be employed to collect data sets of statistical, spatial, time series, etc. nature and defined data quality standards (actuality, accuracy etc.). It builds on the work of D2.2 and the results are presented in D3.1. In the various research projects reviewed by D2.2 a multitude of theoretical approaches related to research and to the choice of methodology were followed. In the elaboration of the methodology for RURALJOBS a combination of positivist and interpretativist/constructivist approaches was taken into account. During this process of elaboration of the methodology several quantitative-qualitative (QQ) methods were analysed and chosen to be used during the different phases of the research, methods that have been used during the last decades in research projects that had similar topics and similar aims.
The main method proposed for task 5.1 'Pilot areas research' is case study. The case study approach is complex and includes a variety of other, quantitative and qualitative methods. In our methodological approach the case study report (which will be the pilot area report), will be based on secondary analysis of statistical data and relevant literature about the pilot area (reports, monographs), semi-structured / in-depth interviews with 20 key informants and structured interviews for recording information about the good practices identified in the pilot area (successful initiatives for employment creation). The quantitative and qualitative data will allow us to draw preliminary conclusions about the rural employment situation in the pilot areas.
The methodology for task 5.2 Strengths, weaknesses, opportunities, and threats (SWOT)and Strategic orientation (SOR) analyses for reference and pilot areas' was already foreseen by the project proposal: based on the results of the research carried out in task 5.1 SWOT analysis will be conducted, followed by strategic orientation round analysis to evaluate the employment development potential of the pilot areas. D3.1 can add very little to these: testing out the adequacy of the proposed methods through the literature review and identifying focus groups as the method to validate the results obtained.
The present deliverable proposes for task 5.3 'Scenarios for pilot areas' a combination of two methods. The logical framework approach is widely used in development projects, but according to our knowledge, it has not been used as a research tool so far. The first part of the analysis required by this approach will be carried out already in tasks 5.1 and 5.2. Within task 5.3 we build up in each pilot area, using the already existing information, a problem tree, an objective tree and a logframe, as parts of a possible strategy for employment growth. Scenario planning is a method for learning about the future by understanding the nature and impact of the most uncertain and important driving forces. Alternative scenarios will be developed and, at workshops gathering key experts, the strategies generated in the previous stage will be tested against each of the scenarios.
By drawing comparisons across reference areas, initiatives and programmes identified as being effective in a pilot area in one reference area could be considered for pilot areas with of a similar type in other countries. Hence good practice can be extrapolated across the EU taking into account local rural culture and practices.
Task 3.2 - Indicator analysis and calibration (Task leader: UOP; participants: all core partners)
D3.2 describes the work of RURALJOBS task 3.2 'Indicator analysis and calibration'. It builds on the work of WP2, particularly deliverables D2.1/b. and D2.4. It uses DPSIR (driving force, pressure, state, impact and response) model as a tool to show the link between 'driving forces' which affect employment and policy responses. This model has been widely used with environmentally oriented indicator sets but has not previously been successfully applied to rural employment indicators.
The driving forces (or 'needs'), which influence the demand for workers and the supply of the workforce, and which represent targets for policy can be 'endogenous' (human, social, financial, natural or physical capitals) or (neo-) 'exogenous' (investors, market, knowledge centres, government and cultural assets). They act on the labour market or employment ('state') through the 'pressures' of jobs (economic activities) and people (the labour force). In turn, the employment rate (jobs per person) and associated parameters influence the 'impact' (sustainable economic prosperity, the key objective of the EU SDS). 'Responses' can be policy responses or socio-economic responses including commuting, migration, business relocation, etc.
Indicators were chosen on the basis of a series of strategies and programmes relevant to employment in rural areas in the EU, mostly identified by D2.1/b. This approach, rather than attempting to develop new indicators, should ensure that the indicators are widely recognised and Specific, measurable, attainable, results-oriented and timed (SMART). Essentially, context indicators were used to define driving forces, pressures, state and impacts, while indicators developed for the 'intervention logic' of programmes were used to identify responses.
A set of 40 indicators was compiled. Of these, 14 independent, policy-relevant indicators were selected to illustrate the range of endogenous driving forces which can have an impact on employment in rural areas (data not shown). Of a further 14 indicators, four describe pressures, six describe state and four describe impacts (table 1). A further eight (data not shown) are supplementary indicators of state. Finally, four indicators of socio-economic responses are identified (proportion of long-distance commuters; net migration; business creation and development; and attractiveness of the area). No indicators of exogenous driving forces appear in the final set, for reasons explained in D3.2 and the response indicators were used to produce a list of 'intervention topics' for which WP5 will identify examples of 'operational good practice'.
It is recognised that indicators which may, for example, be available at NUTS3 level in the Eurostat database will not necessarily be available according to the same definition at a more local level (such as LAU2 and LAU1) in all EU Member States. Only for the 'core' set of 14 pressure, state and impact indicators will RURALJOBS partners be required to collect comparable quantitative data, in order to address the WP5 research questions 'Is there a rural employment problem in the pilot area and, if so, what form does it take'? The other indicators have been used to define the focus of the desk-based research, and the qualitative research described under D3.1 such as interviews and focus groups.
In conclusion, WP3 has worked out the proper tools adapted to the (human and economic) resources and scientific specificity of each country. The methodological toolkit elaborated here could also be used (and applied) in other European rural regions in the future for similar analyses.
WP4 - Typology for regions (task leader: LUA)
The objectives of WP4 are to:
- review existing typologies of rural areas;
-define the typologies of the reference areas being studied by RURALJOBS;
-choose the suitable criteria for different regions;
-perform cluster analysis that to group the different types of regions;
-visualise the results into maps;
-interpret the results and conclusions of WP5 in the broader context of rural typologies.
Task 4.1 - Review of typologies (task leader: LUA; participants: all core partners)
Rural areas are very varied in accordance with spatial, social and economic dimensions and they are not homogenous. For this reason many different typologies have been developed that to determine the basic characteristics of rural areas and support different development policies. Typology of rural may be developed and applied the following uses:
1) for scientific knowledge of rural areas;
2) to administrate the different development policies of rural areas in EU;
3) to implement region or other local development programmes;
4) endogenous rural development.
There are many different typologies of rural areas which were prepared to support rural and labour market development. They are different according to its purpose, methodological principles, methods and others characteristics. The main types of typologies were identified for review of existing typologies of rural areas.
Rural areas typologies were analysed according to the methodological principles:
- policy measures/analytic tool;
-mostly, typologies are designed to discover similarities in regional structures;
- spatial/ performance;
-conceptual/ empirical;
-broad/ narrow;
-macro or micro/input-output/cost-benefit analysis/multicriteria analysis;
already implemented/ can easily be expanded/ would be rather difficult to expand/ would be impossible to expand;
- effective/neutral/ineffective.
The classification of rural areas typologies according to the methods, tools and indicators:
-Binary/multicriterial: A definition of 'rural area' is usually binary (rural versus urban), however for rural areas characterised the multicarerial analysis is used. In this case the typology is often built by more than two categories.
-One/more variables: The differentiating characteristics also vary in the typologies. Often one or more variables are used that to identify the proper characteristic. For instance, the rurality of region can be defined by number of population in the region, by number of population and density, by more than two indicators as in the typology of rural areas in the CEE New Member States.
-Static/dynamic: Static typologies does not account for the element of time, while a dynamic typologies does. Dynamic typologies typically are represented with difference equations or differential equations.
- QQ assessment: The criteria used should not just relate to outputs or factual information, but should incorporate more qualitative assessments to allow for a true picture to emerge. For example, the emphasis within the Canadian research on institutional capacity allowed for the consequent typologies to consider current circumstances and potential future directions. Such an approach would allow for perceptions of place and community priorities (possibly as defined through community planning) to be built into the analysis.
-Disaggregate/aggregative approach:Widely two broad 'families' of methods are used in the rural areas typologies - disaggregative and aggregative approaches. The disaggregative, where the one indicator is viewed as a single large group at the outset, to be progressively split into groups according to pre-selected discriminatory criteria (for example OECD rural areas typology), and the aggregative approach, where the types of rural areas are formed by similar characteristics of territories.
-Disaggregative approaches are less commonly used because of few statistical procedures are available that to categorise the indicators. They are essentially deductive and hardly can be validated.
-Aggregative methodologies associate with factor analysis to reduce a large number of variables to a few key dimensions, and by cluster analysis, to group the cases (rural areas) according to their pattern of scores on these dimensions. This approach may be viewed as 'inductive', since the clusters are determined by mathematical procedures, and the operator has no direct control over the character of the types which emerge.
-Simple deduction/ Principal components analysis (PCA): Methods are highly varying, from simple deductive methods based on setting threshold values for types to multistage methods and PCA.
-Rural/ rural and urban: Usually, both various rural types and various urban types are used.
- NUTS1/NUTS2/NUTS3/LAU1/LAU2. Most of the typologies based on the national (NUTS1- 2) and the regional levels (NUTS3). We can find the typologies build on the lowest administrative unit (LAU1-2) like: 'Scottish executive urban rural classification 2003-2004', 'Typology of rural areas in Italy and its role in strategic programming', 'Rural area typology in Finland', and others. The choice of administrative units depends on the purpose of typology and the official data which is linked with NUTS levels.
-2 types/n+1 types. The number of distinguished types is at minimum two, but often more types are distinguished, up to nine.
-Single/EU-wide typologies. The typologies can be split to the two groups: those which are EU-wide, and those which covering single (or groups of) Member States.
According to the defined characteristics over 60 typologies, which could potentially be applied to rural areas in the EU, were reviewed. The analysis of the existing typologies let present the following conclusions:
1. The main purpose of rural areas typologies is to ensure the policies objectives for rural areas which are multi dimensions; however the typologies are built on one or two criteria.
2. Most of the typologies are built as a rural development policy development tool but the close linked to the policy objectives and the indicators, which reflect the extent of regions achievement of strategic goals, are missed.
3. The indicators of rurality, peripherity and remoteness are dominated in the reviewed typologies.
4. Most of typologies development and empirically tasted models are carried on independently.
5. The typologies mainly developed on official statistical data which is linked with NUTS2 or NUTS3 levels. The most usage sources of statistics were: EUROSTAT, Organisation for OECD database, 2001 census results and some national statistics, however limited for whole EU-27.
6. The extended version of the OECD typology which distinguishes between peripheral and accessible regions within the predominantly rural and significantly rural categories can be used for RURALJOBS project typology development.
Task 4.2 - Define the typology of the regions and pilot areas
The deliverable of this task (4.2) is the calibration of the demographic and socio-economic criteria will be used to select the most appropriate criteria and indexes for defining the typology of the pilot areas (probably LAU1 level or equivalent) that will be studied in WP5. Data from EUROSTAT, national and regional statistics, plans, regional investigation results will be used that prepare the regional types of labour market.
According to the conclusions of review of typology literature, there are a lot of indicators, indexes and criteria which can be used for analysis of regions and their employment system development. Each indicator or criteria has its own characteristics which define availability and suitability for certain analysis. One of the major characteristics is regional level chosen for the typology. For the purpose of the typology of EU-27 regions NUTS3 level was chosen. Regions of NUTS0 to NUTS2 category are too broad for the analysis of local employment system. The very smallest administrative units (LAU1 and LAU2) has relatively poor list of data available for all EU-27 countries. Also the number of the LAU1 or LAU2 regions significantly bigger than NUTS3 regions, thus it would take significantly more resources for analysis and effectiveness of such analysis is uncertain. There are 1303 NUTS3 regions in EU-27 and over 40 indicators available for the analysis on NUTS3 level. So NUTS3 level is an optimal for creation the regional typology for the RURALJOBS project.
A list of 21 indicators was generated for selection of the criteria and indicators which are most suitable for the project. Indicators are available on Eurostat database and from previous studies on rural and regional development. The indicators on the lists are:
1) population (thou);
2) population density (habitants / km2);
3) economically active population (in thousands);
4) share of economically active population of total population;
5) gross domestic product at current market prices;
6) GDP per capita;
7) GDP per capita % EU average;
8) GVA in agriculture and fishing;
9) GVA in industry;
10) GVA in services;
11) share of GVA in agriculture of total GVA;
12) share of GVA in industry of total GVA;
13) share of GVA in services of total GVA;
14) unemployment rate;
15) employment (in thou) in agriculture and fishing;
16) employment (in thou) in non-agricultural sectors;
17) share of employment in agriculture and fishing of total employment;
18) share of employment in non-agricultural sectors of total employment;
19) bed-places in hotel;
20) % of NUTS3 population living within 45 minutes driving time from centroids of cities with at least 50 thou inhabitants; and
21) rurality index.
Rurality index measures a share (%) of total population of the region living in the rural municipalities. The generated list of the indicators was analysed using PCA. According to the results of the PCA ten factors (called 'latent' factor) were excluded. Factor 1 'explains'GVA in industry by 91%, GVA in services by 86%, employment in non-agricultural sector by 97%, economically active population by 97%, population by 96% and GDP by 89%. That means there is strong causal-effects relationship among these indicators and they could be explained relatively very well by single 'latent' factor. The rest of the 'latent' factors explain much less correlations between the indicators.
Indicators that could be hardly explained by latent factor were selected for the typology generation process. The selection is based on presumption that such indicators have weak correlation among each other and combination of such indicators reveals more valuable information about the region. Those indicators are:
1) accessibility;
2) unemployment;
3) rurality index;
4) GDP per capita (or GDP per capita compared to EU average);
and 5) bed places in hotels and similar places.
Unemployment depends on short and long term changes such as seasonal, frictional and structural unemployment. Short term factors has significant share in total numbers, thus unemployment is rather short term indicator than long-term. Typology has to be based on long-term (or so called structural) indicators thus unemployment is excluded from the further analysis.
GDP per capital and bed place in hotels indicate the level of economic activity there, but the former is more aggregated one while the latter is more specific. GDP per capita better fits for the objectives of the typology for this project. And as there is no difference in using GDP per capita or GDP per capital as compared to EU average, the latter is used because it is more convenient for the grouping of the regions.
Based on the literature review and statistical analysis a conclusion was made that the typology of NUTS3 regions in EU-27 shall be developed by using the rurality index, accessibility, and GDP per capital as compared to EU average criteria.
Rurality index is useful for the grouping regions as rural or urban. The indicator is not common in the studies and projects on the Rural and Agriculture development issues and in typologies. Cook and Mizer used an indicator of population number in towns of more than 2500 population.
Rurality index was developed by OECD. Share of population living in the rural municipalities reflect better picture of population distribution compared to population density. Densely populated regions could be rural in terms of geographical scattered living area. Also low population density regions could be urban because of a population living in cities which are surrounded by large territory of wild nature.
Accessibility (i.e. access to city, in minutes) reveals the possibility for the local labour force to get job in the neighbourhood areas. Dijkstra and Poelman used three benchmarks for this indicator, namely 1) 30 min., 2) 45 min, and 3) 60 min. of driving time by car to the city of more than 50 thou habitants. Based on the data collected from previous studies the Rural Jobs project uses 45 min benchmark. The results revealed the tendency that the better develop road and railway network in the region, the more developed region is.
GDP per capita, % of EU average indicates how different development of economic system and living conditions are among the regions. It is a structural indicator which does not change rapidly in short-term and thus is useful for generating typologies. For the RURALJOBS project objectives there are two groups of regions divided by this indicator: 1) 'developed countries' includes regions with GDP per capital 50% of EU average and less, and 2) 'developing countries' includes the regions with GDP per capital 50% of EU-27 average and more. According to the results the GDP per capital divided EU-27 regions into two majors groups, i.e. 'Western and northern Europe' and 'Central and eastern Europe'. In other words the indicator explicitly divided New Member States and Old Member States.
The whole set of these three indicators generates typology which allow to group the regions according to the characteristics of the area, i.e.:
1) how much population lives in rural areas;
2) how the regions are well developed in economic sense;
and 3) how large 'labour market territory' is for the local habitants. Using selected benchmarks of each indicator 12 types of regions were calculated.
Because of limited number of reference areas a modification was made, that is number of types were reduced from 12 to 7. The seven types are:
1) predominantly rural - remote - developing region (3% of total number of NUTS3 regions);
2) predominantly rural - remote - developed region (8 %);
3) predominantly rural - accessible - developing region (6 %);
4) predominantly rural - accessible - developed region (15%);
5) significantly rural - developing region (6%);
6) significantly rural - developed region (29%), and
7) predominantly urban (32%).
The types of EU-27 regions were visualised into the Geographic information system (GIS) maps.
Task 4.3 - Regional foresight scenarios
The main objectives of the task 4.3 were to:
(a) review the methodologies for forecast scenarios building;
(b) propose the methodology based on demographic, economic and labour market data available at Eurostat data base to compute regional forecasts;
(c) prepare the forecast scenarios according to RURALJOBStypology for EU-27 regions at NUTS3 level.
The new methodology to compute labour market forecasts for EU-27 at regional level have been proposed by LUA researches. Autoregressive integrated moving average (ARIMA), Exponentially weighted moving averages (EWMA) and structural-component (SC) estimators methods were integrated into the purposed forecasts model. The outcome of the labour market was estimated by the value of a GVA in the following five sectors according to NACE classification:
1) agriculture, hunting, forestry and fishing (AB);
2) industry and constructions (CF);
3) wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods; hotels and restaurants; transport, storage and communication (GHI);
4) financial intermediation; real estate, renting and business activities (JK); and
4) public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons (LP).
The data to compute regional forecasts was used from the period 1995-2007 and forecast period 2008-2020.
The empirical application of short-term forecasts for employment in 1303 EU regions provide the following results (findings):
Type 1 - Predominantly rural/ Remote/ Developing - 39 EU regions (3%).
Share of employment in agriculture and fishing is expected to decrease to quarter of the total employment in type 1 regions. The decline of the share shall follow the steady pace of 2.5 percentage points on average per seven-year period. The biggest beneficiary of the 'loss' of the agriculture shall be sector of wholesale and retail trade, repair of motor vehicles, motorcycles and personal and household goods, hotels and restaurants, transport, storage and communication (GHI). During the period of 2010-2020 the share of GHI sector shall increase by 1.7 percentage points to 21.3. It is expected to be the fastest growth in type 1 regions.
Type 2 - Predominantly rural /Remote/Developed - 109 EU regions (8%).
The average share of employment in agriculture and fishing in type 2 regions are twice smaller compared to the type 1 regions. This sector is expected to face further strong decline by 2.2 percentage points in 2010-2020. Continuing technological innovations is expected to force farmers to increase the efficiency of a labour force which is scarce in remote regions. The externality of such innovations is a decreasing number of jobs available in agriculture and fishing which one of the most important economic activities for rural viability. Public administration and defense (LP sector) sector would rally in terms of employment creation in type 2 regions during 2010-2020. The share of LP sectors is expected to increase to 32.8 % in 2020. Rapid developments of public sector in the type 2 regions would generate faster growth for financial intermediation, real estate and other business activities compared to other sectors.
Type 3 - Predominantly rural /Accessible/Developing - 77 EU regions (6 %).
Share of employment in agriculture and fisheries is expected to decrease by 2.5 percentage points from 25.8 % in 2010 to 23.3 % in 2020. The loss of jobs in agriculture sector would be compensated by the new jobs in transport, retail, communications and public administration sectors. Share of the employment in transport, retail and communication sector would grow by 1.5 percentage points while in public administration by 1.4 percentage points during 2010-2020. Predominantly rural areas in less developed regions face convergence effect when changes in structure are more volatile. On the other hand developing regions tend to move new jobs creation process to the transport, retail, communication sectors and public administration. Having in mind the availability of human, social and physical infrastructure in type 3 regions, it is hard to expect rapid breakthrough in industry of business services.
Type 4 - Predominantly rural /Accessible/Developed - 198 EU regions (15 %).
Most volatile changes in employment structure are expected in type 4 regions. Rural development policy has a strong impact on changes in employment structure there. Relative less number of jobs is expected to be available in agriculture and industry and relatively more number of jobs is expected in services. During 2010-2020 the share of employment in industry would decrease by 3.6 percentage points to the 25.4 %, the share of employment in agriculture and fisheries down by 2.0 percentage points to 5.7 %. Instead the share of employment in transport, retail and communications is expected to increase by 2.1 percentage points to 25.5 % and will overcome the number of jobs in industry. Similar growth is expected for public administration employment. In 2020 it is estimated to have almost 1/3 of jobs in type 4 regions. Employment in financial and other business services is expected to increase to 10.8 % in 2020. Thus critical role for employment in accessible rural areas with high GDP per capita would play services.
Type 5 - Significantly rural /Developing - 90 EU regions (7%).
Less developed EU-27 regions were going through rapid structural changes in term of employment but relatively less rapid structural changes in terms of GVA. It is expected that 1.5 percentage point decrease in share of employment in agriculture in 2010-2020 to be compensated by the 1.5 percentage point growth in share of employment in retail, transport and communication sector. Loss of the share in industry would be substituted by 0.5 percentage point growth in financial and retail services and by 0.2 percentage points growth in public administration. Relatively modest growth of employment in public administration leads to a conclusion that institutional developments are well integrated into EU market and policy, and that further advancements there are in terms of innovation of the most recent technologies and policies.
Type 6 - Significantly rural /Developed - 377 EU regions (29%).
Compared to type 5 regions, type 6 regions are trended to much more radical institutional changes in public administration. In 2010-2020 it is expected that the share of employment in public administration would increase to 1/3 of all employment in these regions, indicating the importance of state and performance public finances and administration there. Relatively rapid growth in creating jobs will indicate other services (transport, retailers, financial intermediation, real estate and etc.). The biggest loser in terms of the changes in share of employment in type 6 regions is expected to be industry with 3.6 percentage decrease during 2010-2020. Share of employment in agriculture and fisheries will decrease by 1.1 percentage points to the 3.9 % on average in type 6 regions.
Type 7 - Urban regions - 413 EU regions (32%).
Almost one third of all EU-27 NUTS3 regions, are expected to have an average growth of employment in agriculture and fisheries while losses in employment in industry would be compensated by growth in public administration and business consultations. In 2014-2020 it is expected the share of employment in agriculture to decrease insignificantly while decrease in employment in industry would face 2.6 percentage points decline to 20.9 % in 2020. The share of service since 2014 will increase by 3.0 percentage points to 78.0 % in 2020. Rapid increase of employment in services is in line with Europe 2020 strategy to become smarter and more inclusive economy.
Conclusions
Typology of the EU-27 regions is based on complex multi-criteria analysis based on the demographic, economic and territorial criteria which are backed by the data available for NUTS3 level.
Optimal level for the typology of EU-27 regions is NUTS3 level. There sufficiently large number (i.e. 1303) of NUTS3 regions in EU-27 for the representation of socio-economic and demographic situation in the region. Also there are over 40 indicators available for NUTS3 regions on Eurostat and other databases. That ensures detailed and systemic approach for the process of grouping EU-27 regions into certain types.
According to the proposed regional foresight it is expected significant changes in employment structure in rural areas across the EU-27 during the period 2010-2020. Six types of rural areas will face different trends of the changes of the structure, but they have one common feature in common, i.e. rapid decrease of employment in agriculture and fisheries. Thus ongoing process of relaxing labour force from agricultural activities generates economic, social and political stress. The threat of increasing unemployment and degradation of human resources in rural areas shall encourage better focus and better targeting of Rural Development policy measures.
As the estimations of the model revealed attractiveness of the local area would play critical role in developmental processes of the regions. One of the most important factors of the attractiveness of the local area is quality of life indicated by GDP per capita level compared to the EU-27. The higher the quality of life a region has the better business demographic dynamics there.
Calculations also revealed a threat of divergent process among urban and rural areas. The level of GDP per capita in urban areas is expected to stay well above the average of the EU-27, while the predominantly rural areas in developing countries will stay at 30 % of the EU-27 average and with more than 2.2 loss of population during 2010-2020.
WP5 - New strategies for employment in pilot areas (WP leader: UoP)
The objectives of WP5 were to identify labour market, demographic and economic trends in a selection of representative pilot areas and to identify employment growth areas in the context of available human capital, skills and adaptability as well as demand for labour and existence of top-down and bottom-up constraints.
The methodology formulated in WP3 will be applied in WP5.
Task 5.1 - Pilot areas research (task leader: UOP; participants: all core partners)
At the time of writing, the RURALJOBS partners participating in task 5.1. were in the process of finalising their pilot areas and arranging interviews with representatives of the following groups:
(a) elected decision makers;
(b) local government experts;
(c) other experts such as academics and consultants;
(d) community organisations and NGOs including churches; and
(e) the business sector, including chambers of commerce and farmers' unions.
Approximately four representatives from each group will be interviewed, i.e. around 20 interviewees in total. Partners were also starting to identify sources of data, preferably official data sets at LAU2 level, for the fourteen indicators listed in table 1. No results are yet available from task 5.1.
Tasks 5.1 - Pilot areas research and 5.2 - SWOT and SOR analyses for pilot areas
The output of these two tasks was combined into one deliverable, D5.1. (Individual reference area reports on current employment patterns and opportunities for, and constraints on, rural economic diversification and identifying examples of good practice).
TheRURALJOBS review of previous relevant research noted many different approaches to defining the boundaries of study areas for field research. Frequently, administrative boundaries (NUTS2, NUTS3 or LAU1) were used. As our research was expected to 'examine the interaction between different types of rural area (peri-urban, remote, high environmental/amenity value etc.) and the evolution of labour markets, travel to work areas and changing work patterns', we opted to use 'labour market' or 'employment' areas. Remarkably, in most countries represented in the RURALJOBS research, evidence was available which allowed these areas to be defined, as follows: 'Travel to work areas' (TTWA) in the UK; 'Local labour systems' (LLS) in Hungary; and 'agglomeration areas' in Bulgaria. In France, a 'Pays' is the result of a collective bottom-up approach with regional approval of its boundary. Only in Romania was it necessary to use an administrative territory (a NUTS3 region) as a pilot area.