CASE Network Studies and Analyses 456 - The Impact of Socio-Ecological Transition on Employment Structure and Patterns in the Context of (Non) Rural Regions

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CASE Network Studies Analyses No.456 – The Impact Socio-Ecological Transition on ...
Materials published here have a working paper character. They can be subject to further
publication. The views and opinions expressed here reflect the author(s) point of view and not
necessarily those of CASE Network.
This report was prepared within a research project entitled NEUJOBS financed by the
European Commission, under the 7th Framework Programme.
Keywords: Employment Structure and Patterns, Urbanisation, Socio-Ecological Transformation
JEL Codes: J21, R11
© CASE – Center for Social and Economic Research, Warsaw, 2013
Graphic Design: Agnieszka Natalia Bury
EAN: 9788371785856
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Contents
Abstract ................................................................................................................................ 5
1. Introduction ................................................................................................................... 6
2. Features of European employment confronting urbanisation and SET .................... 8
3. Data and methodology ................................................................................................ 11
4. Results ......................................................................................................................... 14
4.1. Regional changes in labour structure and patterns .............................................. 14
4.2. Impact of urbanisation on labour market structure and patterns in the context of
the SET ............................................................................................................................ 16
4.3. Impact of the SET on labour market structure and patterns in different types of
regions............................................................................................................................. 21
4.4. Mid-term perspective and policy recommendations ............................................. 23
Conclusions ........................................................................................................................ 25
References .......................................................................................................................... 26
Appendix ............................................................................................................................. 28

Boris Najman is Associate Professor at the Universities of Paris XII, Researcher at Roses
(joint research unit, University of Paris I – French National Centre for Scientific Research,
CNRS) since February 2002. Boris holds a PhD on Labour Mobility and Informal Economy in
Russia and Ukraine from Ecole des Hautes Etudes en Sciences Sociales-DELTA. Prior to
this, Boris also held several positions of Researcher, Lecturer and Associate Professor in
Parisian Universities and abroad (Serbia, Ukraine, Romania). His research fields include:
labor market survey, household income and budget expenditures survey, poverty, informal
economy studies, labor mobility and Eastern Europe.
Alexander Neumann is a senior expert and researcher in the field of employment and the
labour market and has published extensively on this subject. Since 2009, he has been a full-time
researcher within the Institut für Sozialforschung in Germany. Prior to that, he lead an
inquiry for the Conseil supérieur consultatif d’EDF in Paris and were a full-time researcher in
the Department of Sociology and Psychology at the University of Rouen. He has participated
in projects of the EU’s 5th, 6th and 7th Framework Programmes. Dr. Neumann has
contributed to several projects carried out by CASE, including: ‘NEUJOBS - Employment
2025: How will multiple transition affect the European labour market’ and ‘Poland, Germany:
Review of the Working Time Directive 2003/88/EC: measuring administrative costs and
burdens of various possible options’. He earned his PhD in Sociology from the Faculty of
Social Sciences at Sorbonne University – Paris V.
Izabela Styczynska, Ph.D., is an economist and author of publications in the fields of labour
economics, social policy and health economics. She holds a MA degree in economics from
Warsaw University, Master in Economics at the CORIPE Piemonte in Turin and she has
obtained a Ph.D. at the University of Turin. She has worked for CASE-Center
for Social and Economic Research since 2005, participating in its numerous projects
including: NEUJobs (Employment 2025: How will multiple transitions affect the European
labour market), ANCIEN (Assessing Needs of Care in European Nations) or AIM (Adequacy
of Old-Age Income Maintenance in the EU). She is fluent in English and Italian and has a
working knowledge of Russian language. email: Izabela.Styczynska@case-research.eu
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5
Abstract
This paper focuses on emerging labour patterns within the Socio-Ecological Transition (SET),
with particular attention paid to the effects of urbanisation. Based on the European Labour
Force Survey (ELFS), we mobilize micro-econometric approaches in order to understand
three major employment patterns: job mobility (between unemployment, inactivity, and
employment), the desire to change jobs, and underemployment (i.e. part time jobs) in the
European Union.
The results show that the urbanization transition might express some positive effects on the
labour market in the medium-term for several reasons. The employment rate has slightly
decreased in all types of regions, yet it remains higher in urban settlements. Urban
settlements offer more job opportunities and more part-time employment options. However,
cyclical shocks tend to have a higher impact on urban areas when compared to rural areas.
This means higher chances for employment in urban settlements during a boom and more job
losses during a slow-down (causing less security on the labour market).

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1. Introduction
The structure of employment is characterized by unceasing changes and economists are
constantly trying to understand these shifts. Employment has already been analysed from
various perspectives (skill-biased technological change, job polarisation, changing demand
and several others). In recent years, it has become apparent that more complex approaches
are needed.
The most recent study by Brewer et al. (2012) analyses medium-term changes in
employment structure taking into account medium-term trends (ageing, rising skill levels,
changes in personnel and occupational characteristics) as well as the on-going financial and
economic crisis. They project that full-time employment will remain the most common status
of workers, but the proportion of total employment accounted for by full-time male employees
is projected to fall. Meanwhile, the proportions of part-time male employees and self-employed
women are projected to grow. Total employment is projected to be lower in the
agriculture, mining, manufacturing and education sectors, while the fastest growth in
employment will be in electricity, gas, water and other miscellaneous business services. For
professional characteristics, specific competences may be less demanded like for managers,
professionals, and technical professionals.
The study by Fischer-Kowalsky et al. (2012) assumes that human labour patterns are
influenced by the socio-metabolic regimes that constantly take place. The authors argue that
the transition away from fossil fuels (that started in 1970s) has changed the traditional well-established
patterns of employment, increasing part-time work, unemployment and
“flexibility”. At the same time, the European labour market is currently facing the serious
problems of ageing and high levels of unemployment among the young and elderly.
Moreover, regional disparities in employment patterns are persistent and employment
problems and successes are often linked to particular regions.
Thus, the question arises about the impact of the Socio-Ecological Transition (SET) on
employment structure in different type of regions. This paper aims to answer this inquiry. In
particular, first, we try to identify significant differences in changes in the employment
structure and patterns between urban and rural regions. Second, we empirically analyse the
impact of urbanization on employment structure and patterns. Third, we attempt to see
whether there are significant differences in the impact of the SET on employment between
urban and rural areas.

We mobilize micro-econometric approaches in order to understand three major employment
patterns: job mobility (between unemployment, inactivity and employment), the desire to
change jobs, and underemployment (i.e. part time jobs). We use the European Labour Force
Survey (ELFS), which provides yearly detailed data on all labour market characteristics, as
the reference dataset in order to distinguish several paths across European countries and
regions. We provide our estimates for five European countries (Czech Republic, Germany,
Finland, Netherlands and Spain) for the 2006-2010 period.
When defining the SET, we follow the work of Fischer-Kowalsky et al. (2012), prepared within
WP1 of the NEUJOBS project. Their comprehensive definition of the SET is not only in line
with the majority of scientific research in this area, but also covers the most important future
challenges for EU regions and for EU policy effectiveness. The authors characterise the SET
using four plausible mega-trends. The first one is the socio-ecological transition: a
comprehensive change in the patterns of social organisation and culture, production, and
consumption that will drive humanity beyond the current industrial model towards a more
sustainable future. The second is the societal transition, produced by a combination of
population ageing, low fertility rates, changing family structures, urbanisation and growing
female employment. The third transition concerns new territorial dynamics and the balance
between agglomeration and dispersion forces. The fourth is a skills (upgrading) transition.
In order to analyse the impact of urbanisation transformation on labour patterns, we follow the
classification of urban-rural regions created in the de Beer et al. (2011) report prepared within
WP8 of NEUJOBS project.
The structure of the paper is the following. In the first chapter, we present selected features of
the European employment confronting urbanisation and SET. The second chapter describes
the data and methodology applied, data shortcomings, and problems we experienced with the
estimation. In the third chapter, we present estimation results together and policy
recommendations. The concluding remarks are presented in the final chapter.
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8
2. Features of European employment confronting
urbanisation and SET
It is a well-established fact that regional disparities in employment performance are found to
be persistent, and employment problems and successes are often linked to particular regions.
The latest OECD Regions at Glance series (2011) reveals that around 40% of OECD GDP,
employment, and population growth in the past 15 years has largely been attributed to a small
number of regions (the top 10% of the highest performing regions). The distribution of the
employment rate in the EU-27 reveals that the highest regional employment rates in the EU
are in northern and central Europe, particularly in Germany, the Netherlands, Austria,
Sweden, and the UK. The lowest regional employment rates are found in the southern
regions of Spain and Italy, as well as in some regions of Belgium, Hungary, Malta, Poland
and Romania. While disparities in employment and unemployment rates between countries
have tended to decrease, regional disparities within countries are more persistent. The
dispersion rate, measured by the coefficient of variation, generally followed a downward path
through 2007. Then this pattern reversed, with the dispersion rate increasing for three
successive years through 2010.
Differences across regions are factors behind the observed regional disparities in
employment rates. According to the OECD (2011), they seem to be mostly driven by the
capacity of regional labour markets to generate new jobs, rather than by labour supply or
demographic factors. This fact, however, does not mean that supply-side factors do not
intervene. Depressed regions tend to experience both higher unemployment rates and lower
participation rates than their better performing counterparts. In addition, discouragement
effects are likely to occur in regions where job creation is lagging and unemployment is high.
Differences across regions in the average educational attainment of the working-age
population are another possible factor at work. Regions where unskilled labour is relatively
abundant are likely to be disproportionately affected by skill-biased technological change.
Differences in the age structure of the working-age population seem to play only a very minor
role in most OECD countries in accounting for regional disparities in employment rates.
Another very important explanation of regional disparities in employment is the agglomeration
effect. Firms and people benefit from being located in areas endowed with natural
advantages such as raw materials, availability of fertile soil, suitability of weather conditions or

easy access by land or water. Additionally, firms may benefit from being located near many
other firms if the scale of the economic environment adds to productivity, that is, if
agglomeration generates external economies.
Also, the role of the initial sectoral specialization is assumed to be relatively important in
countries where regional disparities are high. Since employment growth tends to be less
dynamic in some sectors, such as in the agriculture and some manufacturing sectors, than in
others, employment growth differentials at the regional level may simply mirror differences in
initial sectoral specialization. When looking at a classification of the three sectors (agriculture,
manufacturing and services), most empirical analyses suggest that the industry-mix provides
only a partial explanation of regional variations in employment changes. One possibility is to
look more carefully at the type of settlement.
Very few studies analyse the urban-rural differences in employment opportunities. Initially,
rural areas were discussed in the context of employment losses and population decline. Since
the 1970s, researchers have been pointing out that a substantial urban-rural shift is occurring
in the distribution of both population and employment opportunities (see for example Berry et
al., 1973, or Vining et al., 1977). Furthermore, the EC study (1997) shows that some rural
regions are among the most dynamic of the European Union, and that they have been more
successful in generating a higher level of new employment opportunities than the national
economies as a whole.
Nevertheless, some critics see the counter-urbanisation as a construct resulting from an
inadequate area typology where the growth and sprawl of urban areas is counted as rural
growth by mistake (Koch, 1980). In fact, depending on the time period, region, and
methodology, different estimations and assessments can be proven. An additional problem
found in the literature is the difference in classifying urban and rural areas. For example, the
study by Kiehl et al. (2002) investigates the continuity and the driving forces of the urban-rural
employment shift in Western Europe. They use the typology of urban, semi-urban, and rural
areas for three European countries, namely Great Britain, Germany and Italy. According to
their study, these countries are characterised by a long-term redistribution of employment in
favour of rural areas. The peak of counter urbanisation was reached in the 1970s. Rural
employment growth in the 1980s and 1990s occurred at relatively low levels. A general and
strong polarisation trend across the area typologies (agglomerations, semi-urbanised areas
and rural areas) could be seen.
On the other hand, the EC study (2006) on employment in rural regions reveals that
economic activity rates are, on average, slightly higher in urban regions than in rural regions.
A low rate of economic activity is observed due to ageing populations or barriers to
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employment, which particularly affect older women. Moreover, the economic activity rate is
rather stable in rural areas over time, while only a small increase is observed in urban areas.
The level of economic activity is largely a function of three supply-side factors: gender
differences, age structure, and worker discouragement. Employment rates are also generally
higher in urban regions than rural regions.
Some interesting studies examine the impact of the SET on the labour market. Fischer-
Kowalsky et al. (2012) attempt to find linkages between the SET and labour. They look at the
human labour patterns in four different socio-metabolic regimes, namely the agrarian regime,
the coal-based industrial regime, the oil based industrial regime II and the transition phase
away from fossil fuels (early 1970s onwards). They consider human labour on three levels:
1) Qualitative changes in labour: critical capacities of human labour,
2) Quantitative changes in labour: how much of the collectively existing human
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lifetime is spent on labour, and,
3) Changes in the institutional form of labour.
They find that in the last regime, working time per employee continues to decline in Europe.
They argue that this is rather a symptom of increasing part-time work (particularly by women),
unemployment, and rising “flexibility”. More generally, they admit that there are signs of
erosion of traditional well-established patterns of employment, and rising insecurity, while no
clear-cut new pattern has emerged.
The European Commission’s report on future challenges for EU regions, including changes in
employment (EC, 2008), identifies adapting to globalisation, demographic change, climate
change, as well as energy challenges as potential roadblocks. As stated in the report, the
shrinking working age population, an ageing society, and population decline will have a
marked effect on many regions. Regions in demographic decline are often characterised by
relatively low-income levels, high unemployment, and a large proportion of the workforce
employed in declining economic sectors. They tend to have a relatively small proportion of
young people, due to their migration to other areas, as well as low population density and low
growth potential due to the shrinking labour force. Other regions, in particular metropolitan
and some coastal areas, will gain in population. Metropolitan regions are projected to face
high levels of inward migration of the working age population while remaining primary
destinations for international migration. Demographic changes are therefore likely to reinforce
regional disparities in employment growth as well as economic growth in certain areas.
The aim of the ESPON project on “Demography and Migratory Flows Affecting European
Regions and Cities” (2011) was to assess future changes in population growth, the size of
labour force, and population ageing, and to explore different policy options aimed at

increasing regional competitiveness and territorial cohesion. The project was developed for
selected European countries. The analyses of trends between selected regions revealed
significant changes in the regional labour force. If life expectancy continues to grow, the
number of persons aged 65+ in those selected regions would increase to 111 per cent. To
address these challenges, intra and extra European migration will become increasingly
important. Only under favourable economic conditions, if extra-European migration is high
and if the activity rate increases, will the total size of the labour force increase until 2050.
Even under these favourable conditions, 35 to 40 per cent of all NUTS2 regions will face a
decline in the size of the labour force over this period. If the economic conditions are poor, 55
to 70 per cent of regions will experience a decline in the labour force by 10 per cent or more.
This is especially problematic for rural areas. In most regions in the eastern and southern
parts of Europe, the labour force may decrease by more than 30 per cent. In order to attain
the goals of regional competitiveness and territorial cohesion, policy makers have to cope
with these challenges.
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3. Data and methodology
Data. The statistical data used for this analysis comes from the EU-ELSF database, which is
the only EU-wide source providing economic, labour market and social information. In
addition, it has the advantage of being homogeneous across countries, as in the definitions of
variables in the criteria.
Nevertheless, there are some important limitations to this study linked to the availability and
quality of the available information. The ELFS does not allow us to describe new job patterns,
since the data are not detailed enough by sectors and professions. Job mobility, or the
destructive creation of jobs, appears within the sectors and not simply between sectors.
Professional variables (ISCO) do not take into account the transformation of job tasks and are
also highly aggregated. Additionally, the EU-LFS data has measuring errors that occurred
during data collection and which cause differences among the recorded values of variables.
Biases also emerge due to non-responses (see Rendall et al., 2003), the reluctance or refusal
to cooperate with the survey, as well as the absence of people residing in a household.
As there are no core variables directly linked to the SET within the ELFS, we consider job
mobility in order to produce results on new jobs. We address this issue by looking both at the
macro data from Eurostat and by using individual data from the EU-LFS database. We are

able to study mobility between jobs and even geographical mobility because some questions
from the EU-LFS address the former labour status of the individual surveyed (1 year prior).
Due to data availability (some information is optional in ELFS) and software constraints, we
were not able to cover all European countries. However, the countries under scrutiny
constitute an interesting sample in Europe; we chose some peripheral countries like Spain
and Finland and also included countries from the core of the EU, like the Netherlands or
Germany. We also studied countries that have relatively miscellaneous territorial and/or
population size. At the same time, our sample does not include the Czech Republic, a
representative of the New Member States. Furthermore, for comparative reasons, the choice
of countries corresponds to the territorial coverage of the study of Styczynska et al. (2012),
prepared within WP8 of NEUJOBS project. Additionally, the full set of variables necessary for
our analysis is available only for the last five years. Consequently, we have decided to cover
the period of 2006-2010.
Variables. In order to create a consistent set of independent variables, we followed the work
of the most prominent papers in the fields of evolution of labour market patterns and the SET,
while taking into account data availability and software limitations.
When creating the set of variables that cover the SET, we based our choice on the work of
Fischer-Kowalsky et al. (2012) (a report prepared within WP1 of NEUJOBS project) and NIDI
et al. (2012) (a working paper presented within WP8 of the same project). Fischer-Kowalsky
et al. (2012)’s comprehensive definition of the SET is not only in line with the majority of
scientific research in this area, but also covers the most important future challenges for EU
regions and for EU policy effectiveness. The authors characterise the SET using four
plausible mega-trends. The first one is the socio-ecological transition: a comprehensive
change in the patterns of social organisation and culture, production, and consumption that
will drive humanity beyond the current industrial model towards a more sustainable future.
The second is the societal transition, produced by a combination of population ageing, low
fertility rates, changing family structures, urbanisation and growing female employment. The
third transition concerns new territorial dynamics and the balance between agglomeration and
dispersion forces. The fourth is a skills (upgrading) transition and its likely consequences for
employment and (in)equality.
The paper by de Beer et al. (2011) prepared within the NEUJOBS project aimed to develop a
rural-urban classification for NUTS 2 regions. We follow their assumptions and, based on
their classification, we created analogous independent variables that describe the
urbanisation transition. Consequently, we analyse the changes in employment patterns and
12

structure in the context of existing urbanisation, while taking into account socio-ecological
changes on the market.
Additionally, within the classical approach of the labour market, we searched for sectors
obviously concerned with the socio-ecological transition towards renewable energies, spare
energy and materials as well as sustainable development, such as agriculture, the
construction sector, transports and care-taking. For the moment, these sectors and related
activities have not yet been clearly defined under the viewpoint of the SET within the EU.
Therefore, we were unable to define completely new sectors, job profiles and regions, which
might be defined as emerging poles, or the potential of socio-ecological jobs within the
existing structures, reconversions and dynamics. It is therefore impossible to properly identify
the ecological transformation at the ELFS level.
Based on the issues described above, we use the following explanatory variables of
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the evolution of employment patterns:
 Individual characteristics (gender, age, educational level)
 Societal transition (changing family structure, population ageing, female labour
activity)
 Urbanisation
 Skills transition and knowledge spill over (short-term training)
 Crisis
 Country dummies
Information covering the societal transition, which is not available in the ELFS (regional
population ageing and regional female labour activity) has been merged with the information
from the Eurostat REGIO database. The urbanisation variable is created based on the
classification of urban-rural NUTS2 regions presented in de Beer et al. (2011). To test the
different possibilities of mobility within the labour market, we use a multinomial regression
analysis. In order to analyse regional differences in employment structure, we follow a logit
model, conditional on the labour status of the previous year.

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4. Results
We aim to connect the quantitative dimension of the SET, as defined by Fischer-Kowalsky et
al. (2012), to the urban/rural dimension as defined in NIDI’s paper under Tasks 1 and 2 of
WP8. We attempt to do this in three ways. First, we try to find out whether we observe any
significant differences in changing labour patterns and structure between rural and urban
regions. Second, we empirically analyse the impact of urbanisation on employment structure
and patterns. Third, we attempt to see whether there are significant differences in the impact
of SET on employment between three types of regions (predominantly urban, intermediate
and predominantly rural).
4.1. Regional changes in labour structure and patterns
The data examined reveals that, on average, the employment rate declined slightly in all
types of regions during the period analysed (Graph 1). It is invariably the lowest in
predominantly rural regions. The unemployment rate showed a slight increase in all areas
and remains at rather comparable levels between different types of settlements. This
phenomenon could be explained by a mismatch between job offers resulting from the existing
transition and the existing skills on the labour market.
As expected, economic activity rates were, on average, slightly higher in urban regions than
in rural regions. The low rate of economic activity observed in rural settlements could be
attributed to population ageing or barriers to employment.

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Graph 1. Regional differences in labour structure, by year
.2 .4 .6
0
2006 2007 2008 2009 2010 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010
Predominantly urban Intermediate Predominantly rural
Employed Unemployed
Inactive Part-time work
Voluntary part-time work Probability to look for a job
Source: Own compilation based on ELFS
While the inactivity rate is stable over time in urban and intermediate settlements, it increased
in rural regions, suggesting that people without employment tend to leave the labour market
instead of looking for jobs. It can be argued that this phenomenon is a consequence of the
initial sectoral specialization. Since employment growth tends to be less dynamic in some
sectors (such as agriculture) than in others, employment growth differentials at the regional
level may simply mirror differences in initial sectoral specialization, discouraging rural workers
from seeking employment.
Part time work is more developed in urban and intermediary areas. It is also in these areas
that voluntary part time work is significant. Temporary contracts are more spread out in rural
areas but they declined more significantly during the crisis. Other types of employment
patterns do not express any significant differences, neither between regions nor during the
period analysed. While individual differences are not captured by aggregate data, we need to
look carefully at the individual data to understand the behaviour of people in the context of the
SET.

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Graph 2. Regional differences in labour patterns, by year
.1 .2 .3 .4
0
2006 2007 2008 2009 2010 2006 2007 2008 2009 2010 2006 2007 2008 2009 2010
Temporary contract Shift work
Evening work Night work
Weekend work
4.2. Impact of urbanisation on labour market structure and
patterns in the context of the SET
In order to understand the impact of the SET on labour behaviour, we present our regression
results for labour mobility (Table A.1), desire to change jobs (Table A.2) and voluntary part
time work (Table A.3).
Labour force participation and the SET
We tested the different possibilities of job mobility within the labour market. In our
econometric model (multinomial logit), the probability of having a job, being unemployed, or
being inactive depends on the individual’s labour status the previous year. We also controlled
for personal characteristics, societal transitions, and country dummies. Additionally, we
attempted to control for the impact of the crisis in 2008-09.

We found that the probability of being employed is higher for men, middle-aged individuals,
and people with a higher education. 1
Changing family structure has a significant impact on the probability of being unemployed or
inactive. People living alone and adults without children have a higher probability of being
active, while their chances of being employed are lower. As expected, a single person living
with at least one child has a lower probability of being employed.
We observe that population ageing in residential regions increases the probability of
individuals being inactive or unemployed. This is not a surprise. We also observe an even
higher inactivity rate and unemployment rate among the elderly than in younger cohorts.
An important finding is the impact of female employment rates on the probability of being
unemployed. When women have access to jobs, the probability of being unemployed for both
men and women is lower.
Knowledge spillover, approximated by training received by a person in the last four weeks,
increases the chances of being unemployed and decreases the probability of being inactive.
This result has to be considered with caution because of the definition of training provided in
the ELFS database. It is defined as short-term training rather than a life-long learning
process. Results tend to confirm this. People who are active on the labour market yet
unemployed are forced to participate in this type of activity. In terms of the SET, it would be
interesting to reinforce the long-term qualifications of both jobs seekers and employees
looking for new jobs.
17
As expected, during a crisis, the probability of being unemployed or inactive increases.
All of the country dummies are statistically significant and explain the country specific effect
on the probability of being unemployed or inactive. Unemployment is not the whole picture. It
is not the only adjustment taking place on the EU labour market. A large part of labour
adjustments to changes affecting demand is done through underemployment (or part time
work). In fact, when we consider the transition matrix between labour statuses in 2009 and
2010, we notice that employment and inactivity are rather stable categories compared to
unemployment. A very large number of people remain in the same category (94.06 % of
employed people and 81.5% inactive). However, 43% of unemployed people in 2009 were
still unemployed in 2010 and 28.75% moved to inactivity (discouraged or retired).
1 Please see the Appendix for Table A.1

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Table 1: Labour Mobility between 2009 and 2010 (in % of 2010 total labour status)
Labour status 2009 Labour status 2010
Employed Unemployed Inactive Total
Employed 94,06 2,77 3,17 100
Unemployed 28,26 43 28,75 100
Inactive 15,45 3,05 81,5 100
Source: Own calculation based on ELFS
Desire to change job and SET
In our sample, around 5% of the working population expressed a willingness to change jobs.
From a theoretical perspective, job mobility can contribute significantly to economic growth
and development. On the other hand, empirical evidence shows that stable employment
relationships and relatively long job tenures are positively correlated with productivity. Results
of the impact of the SET on people’s willingness to change jobs are provided in Table A.2.
The desire to change jobs was higher for women, youth (up to 26 years old) and better-educated
people. Being unemployed 12 months prior reduces an individual’s probability of
looking for another job.2
Individuals living in households with at least two adults and children have the lowest desire to
look for another job, whereas individuals living alone or only with adults are keener to change
jobs. Being trained increases the willingness among employees to look for another job, which
fits with another result we already mentioned previously, when we found that jobseekers may
be supported by gaining long-term qualifications rather than by attending short trainings.
Interesting results emerge when controlling for work conditions. People with short-term
contracts want to change jobs more frequently than people employed on a permanent basis.
This is not surprising, as people prefer to have job stability. At the same time, employees who
found a job after having been unemployed for one year tend to stop looking for another job.
This might underline the fact that the desire for job mobility remains labour market driven,
given that people look for jobs when they have to. Employees doing shift work are also less
interested in getting a new job. Given that shift work is a classical labour pattern in general
practiced within traditional sectors (industries or hospitals, etc.), this confirms the idea that
there is no spontaneous shift towards green jobs.
Another interesting result is shown with respect to population ageing. The older the
population in the region is, the lower its willingness to change jobs. Nevertheless, the job rate

among younger people is low within the EU. The core of the European labour market is still
focused on highly skilled 30 to 50 year olds having stable job positions, as confirmed by our
regression results.
19
Part time work and the SET
As we can see in Graph 3, part time employment is especially developed in the North of
Europe (The Netherlands, Germany, Sweden, Denmark…), where flexicurity was developed.
The concept of “flexicurity” is an important part of the EU’s policy agenda, and is also linked
to the discussion on job mobility in Europe. We can observe that the countries in which the
population is more concentrated in urban areas are also the ones in which part time
employment is booming.
Graph 3. Part-time employment across Europe in 2011
Source: Eurostat, 2012
The regressions show that women, rather than youth and the uneducated, are the most likely
to work part-time. People living in households without children have lower chances of
choosing part-time employment. The same applies for working one-member households with
children. Voluntary part-time employment is highest among individuals that live in households
with at least two adults and children.

When the population in a region ages (i.e. a higher life expectancy is observed), the new
employment organisation is often uneasy to setup with new technologies. Older population
may have more difficulties to learn new competences. 3
20
Table 2. The impact of urbanisation on labour market structure – marginal effects
Predominantly rural
(Ref: Predominantly urban)
Intermediate
Probability of being
unemployed
-0.002*** -0.002***
Probability of being inactive
(Ref: Probability of being
employed)
-0.004*** -0.012***
Desire to change jobs -0.003*** -0.002***
Willingness to work part-time -0.052*** -0.003
Note: Marginal effects are derived from the regressions of labour structure presented in the Appendix (see Tables
A.1-A.3)
Urbanisation increases the chances of voluntary part-time employment (Table 2). Urban
settlements offer more job opportunities than rural ones. Unemployment rates are lower in
large urban areas and because the economy is very dynamic in those areas, the bargaining
power is not so weak for the workers.
An interesting finding is the probability of being employed in rural versus urban regions.
Results show that it is more difficult to be employed in a predominantly urban region, whereas
the probability increases in intermediate or predominantly rural regions (see marginal effects).
The state of Brandenburg in Germany may provide an explanation for this result. While the
urban regions were heavily impacted by the recession in 2009 (i.e. Frankfurt/Oder), rural
areas did not evolve much (i.e. the Uckermark), as they already presented lower job rates
before the crisis. Rural regions could not fall much deeper, while urban areas had many jobs
to lose. At the same time, jobs in rural regions tended to be more closely related to local or
regional markets (agriculture, SME, etc.) and therefore were more protected from the
immediate effects of the global crisis (such as the automobile sector).
Participation in training increases the chances of voluntary part-time employment. Again,
these results do not reflect the real impact of skills transition, but rather the individual choices
to work part-time in order to upgrade one’s qualifications. On the other hand, this is an
important result, which suggests that an elastic labour market encourages employees to
invest their time in Life-Long Learning .

Individuals that were unemployed a year before the survey are more likely to accept part-time
work. Again, labour market flexibility might allow a number of countries to reduce their
unemployment figures and to adjust more efficiently to demand shocks.
Specific working conditions, like weekend, evening, or night work, have a negative impact on
being employed. This is due to the specificity of these types of jobs. In such jobs, part-time
workers usually work 12-hour shifts.
In the next section, we test if these results are similar across different types of settlements
(rural, urban and intermediate).
4.3. Impact of the SET on labour market structure and patterns in
different types of regions
We assume that labour patterns are different across various types of settlements. We think
that individual behaviour on the labour market depends on the kind of settlement. This is
especially important when it comes to labour market changes in the context of the SET.
Here we present the results of the regressions for three main labour market behaviours in the
SET context: labour force participation, the desire to change jobs and voluntary short-term
work.
21
Employment, Unemployment and inactivity across types of settlements (mlogit)
We present the results of the multinomial regression in the appendix (Table A.5). Across
different type of settlements, individual characteristics affect the probability of being
unemployed or inactive in a similar way. We have standard results: women, youth, the elderly
and less educated people have the highest probability of being unemployed or inactive. This
is similar for urban, intermediate, and rural settlements.
The main differences in the results come from the impact of SET variables on labour
market participation. First, in rural areas, the fertility rate does not reduce the probability of
being inactive. On the contrary, in urban and intermediate areas we see a negative effect.
This is probably due to the demographic structure of the population: on average, in rural
areas, the population is older.4

In rural areas, when life expectancy increases, the probability of being inactive also
increases. However, life expectancy has no effect on labour market participation in urban and
intermediate areas.
In rural areas, training in the last month increases the probability of being inactive. It has
exactly the opposite effect in urban and intermediate areas.
Desire to look for another job across types of settlement (probit)
We run a logit regression and we present the results in Table A.6 in the appendix. In all types
of settlements, apart from education, we find similar results for individual characteristics.
Many highly-educated people want to change jobs in urban and intermediate areas, while in
rural regions, this is not significant. There are many more educated people in urban areas
compared to rural areas.
Again, the main differences in results come from the impact of the SET variables on the
desire to change jobs. In rural settlements, the fertility rate does not affect the will to change
jobs, while in urban and intermediate regions, it reduces the probability of wanting to change
jobs.5
In rural areas, the female employment rate decreases the probability of looking for another
job. In the same type of region, training does not affect the will to change jobs.
The 2008-2009 crisis affected rural and urban / intermediate areas differently. In rural regions,
the crisis did not affect the desire to change jobs, while in urban and intermediate regions, the
crisis increased the will to change jobs.
22
Voluntary short-term work across types of settlements (probit)
We run a logit regression on intended short-term work and we present the results in the
appendix in Table A.7. In all types of settlements, apart from gender, we found similar results
for individual characteristics. Women are usually, on average, more interested in part-time
jobs. However, in rural areas, women are indifferent.6
Except for urban areas, the fertility rate increases the probability of intentional short-term
work. On the other hand, in urban areas, the female employment rate increases the
probability of short-term work.
Finally, the crisis reduced the probability of intended short-term work, but only in rural areas.
Elsewhere, the crisis did not have a significant effect. .

23
4.4. Mid-term perspective and policy recommendations
Based on our regression results and using labour surveys, drawing a mid-term perspective is
a challenging task. However, we can highlight the main trends and behaviours observed in
urban areas versus rural areas in the SET context.
First, there has been noticeable growth in short-term work, especially in urban areas. The
opposite was observed in rural areas during the crisis. The desire to have more time for
family and personal development is probably one of the reasons for these changes on the
labour market (Fischer-Kowalsky et al. (2012)).
Second, the development of the services sector is an on-going process, which goes hand in
hand with a highly-educated population.
Third, urbanization is linked to high employment rates, especially for women. In addition a
high fertility rate increases the overall labour force participation rate, mostly for young fathers,
in urban areas.
Fourth, in the SET context, general competence versus more specific competence may help
people find new jobs and be able to move from one job to another. Job to job mobility is going
to grow, especially in urban areas.
The main conclusion from our empirical work with the ELFS is that labour reallocation can be
a strong driving force for the SET. Education and general competences (i.e. not specific to
particular investment) may help the transition from “old” jobs to “new” jobs.
Government policies related to the labour market can help job mobility and short term work.
Indeed, labour reallocation is probably going to happen with time reallocation. More efficient
and productive jobs may require part time work.
The government can push for labour legislation changes, but it can also protect and train
workers in order to ease their labour mobility. .

24
Graph 4. Mid-term technological/skills changes and labour changes
SET and strong
labour productivity
and labour
participation
Changes in
technologies/
skills
Changes in
labour
practices
Working time
reduced, labour
mobility
Large share of
skilled labour
Status quo on
labour patterns

25
Conclusions
During the analysed period, no significant changes in employment structure and patterns
between urban and rural regions are observed. The employment rate is still lower in rural
regions than in intermediate and urban regions. The same applies for the inactivity rate. The
unemployment rate is rather stable and at a comparable level in all types of regions. This
phenomenon could be explained by a mismatch between job offers resulting from the existing
transition and existing skills on the labour market.
Second, the urbanisation transition has had a positive effect on the labour market. First, the
chances of voluntary part-time employment increase in urban settlements. Moreover, urban
areas offer a larger number of job opportunities when compared to rural areas.
Unemployment rates are lower in large urban areas and because the economy is very
dynamic in those areas, the bargaining power is not so weak for the workers.
On the other hand, the urbanisation transition brings a certain level of insecurity to the labour
market. Results confirm that urban regions are more sensitive to cyclical changes in the
economy. Urban regions offer better job opportunities and the possibility to change jobs and
advance in one’s career during prosperous times. But at the same time, they have more jobs
to lose during the crisis. In contrast, rural regions are less influenced by global shocks. They
do not evolve much during prosperous times, as they are more closely related to local or
regional markets, and they cannot fall much during the crisis period.
Across the different types of settlements, individual characteristics affect the probability of
being unemployed or inactive in a similar way. We have standard results: women, youth, the
elderly and the less educated people have the highest probability of being unemployed or
inactive. This is similar for urban, intermediate and rural settlements.

26
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international comparison, Philadelphia, Regional Science Research Institute Discussion
Paper 100.
27

28
Appendix
Table A.1. Multinomial regression by labour status
Variable Unemployed Inactive
Personal characteristics
Women 0,317***
(0,009)
0,464***
(0,006)
Age -0,129***
(0,002)
-0,285***
(0,001)
Age2 0,001***
(0,000)
0,004***
(0,000)
Higher education (Ref: at
maximum lower secondary)
-1,138***
(0,013)
-1,015***
(0,009)
Secondary education -0,663***
(0,108)
-0,644***
(0,008)
Societal transition
Changing family structure (Ref:
At least- two hh members with
children)
One member household 0,332***
(0,013)
-0,318***
(0,012)
Only adults 0,037***
(0,009)
-0,512***
(0,008)
One hh member with
child(ren)
0,511***
(0,022)
0,339***
(0,015)
Population ageing
Fertility rate 3,349***
(0,274)
-4,678***
(0,191)
Life expectancy 0,633***
(0,014)
0,041***
(0,011)
Female labour activity
Female regional employment
rate
-0,199***
(0,806)
0,011***
(0,004)
Urbanisation (Ref:
Predominantly urban regions)
Predominantly rural regions -0,081***
(0,005)
-0,022***
(0,008)
Intermediate regions -0,083***
(0,010)
-0,059***
(0,007)
Skills transition, knowledge
spillover
Training within last four
weeks
0,121***
(0,012)
-0,369***
(0,008)
Other
Crisis period 0,073***
(0,010)
0,079***
(0,007)
Labour inactivity year before -0,988***
(0,009)
3,588***
(0,007)
Czech Republic (Ref:
Germany)
-0,211**
(0,009)
0,856***
(0,066)
Finland -0,299*** 1,803***

29
Variable Unemployed Inactive
(0,092) (0,086)
Netherlands -1,492***
(0,095)
0,419***
(0,067)
Spain -2,833***
(0,068)
0,464***
(0,045)
Source: Own calculations based on ELFS.

30
Table A.2. Probability to look for another job
Variable Coefficient
Women 0,179***
(0,013)
Age 0,108***
(0,005)
Age2 -0,002***
(0,000)
Higher education (Ref: at maximum lower
secondary)
0,161***
(0,018)
Secondary education 0,081***
(0,018)
Societal transition
Changing family structure (Ref: At least two hh
members with children)
One member household 0,434***
(0,020)
(0,015)
One hh member with child(ren) 0,573***
(0,032)
Population ageing
Fertility rate -3,292***
(0,385)
Life expectancy -0,001**
(0,007)
Regional female employment rate -0,000
(0,007)
Urbanisation (Ref: Predominantly urban regions)
(0,019)
Intermediate regions -0,096***
(0,015)
Skills transition, knowledge spillover
Training within last four weeks 0,058***
(0,017)
Work conditions
Short term contract 1,022***
(0,016)
Person usually works during weekends -0,062***
(0,017)
Person usually working at night -0,027
(0,014)
Person usually working in the evening 0,126***
(0,016)
Person usually doing shift work -0,152***
(0,022)
Other
Crisis period 0,084
(0,018)
Labour unemployed year before -1,389***
(0,025)

31
Czech Republic (Ref: Germany) -0,759***
(0,121)
Finland 0,701***
(0,091)
Netherlands 1,832***
(0,135)
Spain 0,204**
(0,089)
No of observations 870 851
R² 0,45

32
Table A.3. Probability to have not constraint short time work
Women 0,492***
(0,020)
Age 0,052***
(0,004)
Age2 -0,001***
(0,000)
Higher education (Ref: at maximum lower
secondary)
-0,093***
(0,020)
Secondary education -0,073***
(0,018)
Societal transition
Changing family structure (Ref: At least two adults
hh members with children)
One member household -2,151***
(0,028)
Only adults -1,685***
(0,017)
One hh member with child(ren) -0,224***
(0,033)
Population ageing
Fertility rate -0,172
(0,0536)
Life expectancy -0,325***
(0,028)
Regional female employment rate 0,034***
(0,008)
Urbanisation (Ref: Predominantly urban regions)
(0,025)
Intermediate regions -0,011
(0,016)
Skills transition, knowledge spillover
Training within last four weeks 0,719***
(0,022)
Other
Crisis period 0,013
(0,514)
Labour unemployed year before 1,222***
(0,048)
Czech Republic (Ref: Germany) -1,012***
(0,136)
Finland 0,078**
(0,012)
Netherlands 0,103
(0,155)
Spain -0,375***
(0,102)
Work conditions
Short term contract -0,506***
(0,021)

33
Person usually works during weekends -0,071***
(0,021)
Person usually working at night -0,036
(0,028)
Person usually working in the evening -0,140***
(0,028)
Person usually doing shift work -0,184***
(0,027)
R² 0,22
No of observations 157 463
Source: Own calculations based on ELFS

34
Table A.4. Selected Descriptive statistics between regions
Variable Predominantly urban Intermediate Predominantly rural
Personal
characteristics
Women 52,14% 51,19% 50,98%
Age 43,91 44,03 45,79
Higher education 24,43% 16,88% 13,21%
Secondary education 38,85% 44,00% 45,41%
At max lower
36,72% 39,11% 41,38%
secondary
One member
household
14,63% 10,17% 10,59%
Only adults 40,86% 42,28% 45,38%
One hh member with
4,62% 3,34% 2,85%
child(ren)
At least two adults hh
members with children
39,89% 44,20% 41,18%
Training within last
four weeks
18,75% 16,10% 14,25%
Regional female
employment rate
61,92% 61,7% 59,2%
Regional fertility rate 1,56 1,55 1,55
Regional life
expectancy
79,34 79,06 78,53

35
Table A.5. Multinomial regression by labour status
Variable
(Ref:
Employed)
Unemploy
ed
Inactive Unemploy
ed
Inactive Unemploy
ed
Inactive
Women 0,180***
(0,013)
0,349***
(0,009)
0,329***
(0,017)
0,438***
(0,012)
0,495***
(0,016)
0,689***
(0,012)
Age -0,129***
(0,003)
-0,264***
(0,002)
-0,133***
(0,004)
-0,299***
(0,002)
-0,128***
(0,004)
-0,311***
(0,002)
Age2 0,001***
(0,000)
0,004***
(0,000)
0,001***
(0,000)
0,004***
(0,000)
0,001***
(0,000)
0,004***
(0,000)
Higher
education
(Ref: at
maximum
lower
secondary)
-1,132***
(0,019)
-1,024***
(0,013)
-1,125***
(0,027)
-0,874***
(0,019)
-1,161***
(0,028)
-1,275***
(0,021)
Secondary
education
-0,652***
(0,016)
-0,610***
(0,011)
-0,629***
(0,021)
-0,557***
(0,014)
-0,730***
(0,021)
-0,815***
(0,015)
Societal
transition
Changing
family
structure (Ref:
At least- two
hh members
with children)
One member
household
0,285***
(0,021)
-0,278***
(0,016)
0,398***
(0,031)
-0,273***
(0,027)
0,361***
(0,031)
-0,473***
(0,027)
Only adults 0,020
(0,015)
-0,465***
(0,012)
0,100***
(0,018)
-0,542***
(0,015)
0,005
(0,017)
-0,558***
(0,015)
One hh
member with
child(ren)
0,576***
(0,030)
0,351***
(0,019)
0,445***
(0,044)
0,374***
(0,029)
0,446***
(0,043)
0,318***
(0,035)
Population
ageing
Fertility rate 3,523***
(0,386)
-5,584***
(0,264)
3,217***
(0,524)
-6,002***
(0,351)
3,425***
(0,599)
0,638
(0,472)
Life
expectancy
0,597***
(0,021)
0,014
(0,016)
0,699***
(0,028)
0,031
(0,022)
0,587***
(0,032)
0,096***
(0,027)
Female labour
activity
Female
regional
employment
rate
-0,184***
(0,008)
0,024***
(0,005)
-0,208***
(0,011)
0,011
(0,006)
-0,233***
(0,014)
-0,049***
(0,011)
Skills
transition,
knowledge
spillover
Training
within last
four weeks
0,170***
(0,018)
-0,524***
(0,012)
0,056**
(0,025)
-0,638***
(0,016)
0,104***
(0,026)
0,333***
(0,019)
Other
Crisis period 0,044*** 0,138*** 0,096*** 0,129*** 0,101*** -0,047***

36
(0,016) (0,012) (0,020) (0,015) (0,018) (0,014)
Labour
inactivity
year before
-0,94***
(0,015)
3,601***
(0,011)
-0,971***
(0,018)
3,710***
(0,014)
-1,049***
(0,017)
3,466***
(0,013)
Czech
Republic (Ref:
Germany)
-0,406***
(0,122)
0,842***
(0,091)
0,102
(0,166)
1,116***
(0,119)
-0,611***
(0,217)
0,043
(0,178)
Finland -0,404***
(0,102)
0,756***
(0,100)
-0,715**
(0,028)
0,775***
(0,087)
-0,414***
(0,032)
-0,711***
(0,071)
Netherlands -1,638***
(0,135)
0,746***
(0,094)
-1,465***
(0,183)
0,954***
(0,125)
-1,368***
(0,214)
-1,429***
(0,163)
Spain -2,677***
(0,093)
0,582***
(0,063)
-2,79***
(0,127)
0,714***
(0,084)
-3,293***
(0,152)
-0,413***
(0,116)
R² 0,536 0,555 0,561

37
Table A.6. Probability to look for another job
Variable/ Coefficient Predominantly urban Intermediate Predominantly rural
Personal
characteristics
Women 0,101***
(0,018)
0,216***
(0,025)
0,386***
(0,033)
Age 0,114***
(0,006)
0,113***
(0,008)
0,072***
(0,010)
Age2 -0,002***
(0,000)
-0,002***
(0,000)
-0,001***
(0,000)
Higher education (Ref:
at maximum lower
secondary)
0,178***
(0,024)
0,185***
(0,035)
-0,041
(0,048)
Secondary education 0,043*
(0,024)
0,129***
(0,033)
0,135***
(0,045)
Societal transition
Changing family
structure (Ref: At least
two hh members with
children)
One member
household 0,417***
(0,025)
0,453***
(0,040)
0,516***
(0,059)
(0,021)
0,040
(0,028)
0,145***
(0,036)
One hh member with
child(ren)
0,482***
(0,043)
0,758***
(0,059)
0,631***
(0,083)
Population ageing
(0,507)
-3,486***
(0,727)
-1,432
(1,089)
(0,029)
-0,100**
(0,043)
0,038
(0,055)
Regional female
employment rate
0,003
(0,009)
0,025*
(0,013)
-0,061**
(,022)
Skills transition,
knowledge spillover
four weeks
0,062***
(0,022)
0,057*
(0,033)
0,045
(0,049)
Work conditions
Short term contract 0,853***
(0,022)
1,129***
(0,031)
1,384***
(0,038)
Person usually works
during weekends
-0,098***
(0,025)
0,037
-0,119**
(0,049)
Person usually
working at night
-0,048
(0,032)
-0,022
(0,042)
0,029
(0,059)
Person usually
working in the evening
0,130***
(0,026)
0,115***
(0,031)
0,133***
(0,046)
Person usually doing -0,128*** -0,166*** -0,209***

38
Personal
characteristics
shift work (0,031)
(0,041)
(0,051)
Other
Crisis period 0,103***
(0,025)
0,061*
(0,034)
0,050
(0,039)
Labour unemployed
year before
-1,430***
(0,037)
-1,434***
(0,047)
-1,160***
(0,047)
Germany)
-0,979***
(0,164)
-0,535**
(0,0227)
-1,215***
(0,352)
Finland 0,741***
(0,122)
0,924***
(0,161)
0,741***
(0,025)
Netherlands 1,913***
(0,181)
1,729***
(0,259)
1,353***
(0,374)
Spain 0,348***
(0,119)
0,544***
(0,167)
-0,836***
(0,248)
No of observations 299 574 192 948 169 425
R² 0,085 0,091 0,133

39
Table A.7. Probability to have not constraint short time work
Personal
characteristics
Women 0,577***
(0,026)
0,518***
(0,037)
0,044
(0,059)
Age 0,041***
(0,006)
0,077***
(0,008)
0,036***
(0,0125)
Age2 -0,001***
(0,000)
-0,001***
(0,000)
-0,001***
(0,000)
Higher education (Ref:
at maximum lower
secondary)
-0,090***
(0,026) -0,137***
(0,037)
-0,003
(0,060)
Secondary education 0,118***
(0,025)
-0,011
(0,032)
0,090*
(0,053)
Societal transition
Changing family
structure (Ref: At least
two adults hh members
with children)
One member
household
-2,199***
(0,035)
-2,369***
(0,058)
-1,380***
(0,099)
Only adults -1,791***
(0,023)
-1,768***
(0,030)
-1,121***
(0,048)
One hh member with
child(ren)
-0,125***
(0,043)
-0,399***
(0,062)
-0,429***
(0,101)
Population ageing
(0,578)
1,302*
(0,722)
3,075**
(1,122)
(0,039)
-0,362***
(0,056)
-0,288***
(0,066)
Regional female
employment rate
0,056***
(0,011)
0,005
(0,014)
-0,016
(0,021)
Skills transition,
knowledge spillover
four weeks
0,824***
(0,028)
0,556***
(0,039)
0,611***
(0,068)
Other
Crisis period 0,043
(0,029)
0,036
0,038)
-0,142***
(0,049)
Labour unemployed
year before
1,202***
(0,072)
1,192***
(0,091)
1,129***
(0,093)
Germany)
-0,367*
(0,191)
-2,071***
(0,249)
-1,465***
(0,353)
Finland 0,514*
(0,201)
-0,357**
(0,086)
-0,748*
(0,201)
Netherlands 0,494**
(0,215)
-0,271
(0,290)
-0,825*
(0,434)
Spain -0,037***
(0,142)
-0,891***
(0,191)
-0,794***
(0,271)
Work conditions
Short term contract -0,507*** -0,470*** -0,659***

40
(0,028) (0,039)
(0,051)
Person usually works
during weekends
-0,052*
(0,028)
-0,136***
(0,036)
0,032
(0,061)
Person usually
working at night
-0,071*
(0,038)
-0,015
(0,048)
0,085
(0,089)
Person usually
working in the evening
-0,146***
(0,025)
-0,170***
(0,032)
-0,054
(0,057)
Person usually doing
shift work
-0,253***
(0,037)
-0,060
(0,048)
-0,174**
(0,068)
R² 0,2315 0,228 0,135
No of observations 63 125 37 265 12 976
Source: Own calculations based on ELFS

CASE Network Studies and Analyses 456 - The Impact of Socio-Ecological Transition on Employment Structure and Patterns in the Context of (Non) Rural Regions

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CASE Network Studies and Analyses 456 - The Impact of Socio-Ecological Transition on Employment Structure and Patterns in the Context of (Non) Rural Regions