4.1.1 Analysing shifts of socio-economic status of inner peripheries within a period of time
One of the methodological tools chosen for the analysis of changes of the socio-economic status of inner peripheries over time is scatter plot analysis. A very illustrative form of analysing shifts of socio-economic positions of inner peripheries compared to other areas could be accomplished, if we set up a coordinate system on a scatter plot representing changes within a period of time. The two axes of the scatter plot represent distributions of a given variable (illustrating certain demographic, social or economic characteristics) in two snapshots of time.
Fields outlined by the coordinate system might be based on points of intersection of zero values or EU28 averages (in two points of time), and they illustrate directions of shifts in the status of units in the analysis, by visualizing generalized tendencies (position have remained the same – bad or good; there is an advancement; deterioration of status can be observed).
While shifts represented by fields of the scatter plot illustrate only relative trends between two points of time, many or most of the changes might happen within categories of regions with disadvantaged or more favourable positions. For the adequate interpretation of these changes a trend line of positive/negative values is also represented on analysed scatter plots, which gives the opportunity of a more detailed evaluation of position shifts of different regions.
Inner peripheries (and/or other special types of regions) might be differentiated in the scatter plot in accordance with the purpose of the analysis, by using different signs and colours etc.
In this way, their position changes could be represented in a very clear way, not just by reflecting the general trends affecting their status, but also by making comparison with other region types. In the presented analyses, groups of inner peripheral areas based in the four delineations were merged, and the differentiation among NUTS 3 units identified as being
inner peripheral was made by their numbers of assignment as IP. Besides, non-peripheral areas were also represented separately.
In the case of indicators which originally express some kind of dynamics by themselves (population change, migration rate), scatter plot illustrations were not used. Instead, tables on typical directions of changes were applied to outline these basic trends.
4.1.2 Analysing socio-economic dynamics of inner peripheries
Positions shifts within a period of time, and the generalised directions of changes between two points of time tell less about the detailed socio-economic dynamics of analysed areas.
Within a period of ten-fifteen years, different directions of changes might be observed related to global, national and regional tendencies. In order to follow these trends changing the socio-economic positions of inner peripheral areas are also analysed based on the comparison of line charts representing time-series of potential key measures associated with phenomena of peripherality.
Since units of investigation in this analysis are single regions, two methodological considerations are followed to keep the accomplishment and the interpretation of analysis and results manageable. Firstly, into this analysis of time-series only regions assigned as inner peripheries were processed. Insights on the development path of inner peripheral regions compared to other regions of Europe might be obtain from analyses of socio-economic position shifts of areas illustrated by scatter plots. The classification of inner peripheral areas according to delineation types is used, but not always directly represented in this analysis.
Instead of that, a common pool of inner peripheries is used here, which contains all regions identified as IP by one or another delineation type regardless their actual assignments (see Figure 1.2 in Chapter 1.2.3). In this way, the union of all delineations serve as the basis the generalised interpretation of different socio-economic tendencies affecting the analysed period. Comparisons though are made on variations between paths of changes of the four groups of delineated inner peripheries for representing the status of given regions from this aspect.
The other consideration taken into account is the use of generalised trends of time series for the analysis. Since the path of development of one or another territory over time might significantly vary by one or another, the unstructured information provided by the great mass of socio-economic time-series data should be adjusted for a meaningful interpretation. During analyses six basic, generalised trends were defined on the basis of data available for the gathered time-frame in the case of indicators used in the analysis. Time-series of
fourteen-• Uptrend
If a (mathematically) positive overall change (>[average rate + Std. deviation value]) can be observed in the region or at least two of the three break-downs of periods can be characterised with significant positive change (>[average rate + Std. deviation value]).
• Mostly sideways with uptrend tendencies
If a (mathematically) positive overall change (<[average rate + Std. deviation value]) can be observed in the region and at least two of the three break-downs of periods can also be characterised with sideways tendencies (positive or negative).
• Change with positive tendencies
Any other regions than ‘Uptrend’, ‘Downtrend’ and ‘Sideways’ with usually a (mathematically) positive overall balance, where a trend change (>half of [average + Std.
deviation value]) occurred within one or another sub-period analysed.
• Change with negative tendencies
Any other regions than ‘Uptrend’, ‘Downtrend’ and ‘Sideways’ with usually a (mathematically) negative overall balance, where trend change (>half of [average - Std.
deviation value]) occurred within one or another sub-period analysed.
• Mostly sideways with downtrend tendencies
If a (mathematically) negative overall change (>[average rate - Std. deviation value]) can be observed in the region and at least two of the three break-downs of periods can also be characterised with sideways tendencies (positive or negative).
• Downtrend
If a (mathematically) negative overall change (<[average rate - Std. deviation value]) can be observed in the region and at least two of the three break-downs of periods can be characterised with a small degree of change (<[average rate - Std. deviation value]).
The general direction of (absolute) change of a given indicator within the analysed period of time determine a lot of the build-up of these categories. The more or less clear-cut Europe-wide tendencies of some indicators (e.g. old age dependency rate, ratio of population with low qualification or the ratio of employed persons in manufacturing) resulted in the absence of certain generalised trends (e.g. no big positive or negative changes).
While categories of uptrend, downtrend and sideways dynamics indicate patterns easily understandable (significant or slight-moderate positive or negative tendencies during the analysed period), generalised paths of trend changes are more complicated to interpret. Here, tendencies of changes regarding the whole period usually also indicate a certain overall increase or decrease of values of different socio-economic measures analysed, but these are broken by different kinds of trend shifts. It might happen that a former tendency ends and the development regarding the analysed measure takes a new direction (e.g. new path in population dynamics). Another typical case, where the general progress is broken for a certain period, but it continues afterwards (e.g. temporary peaks of unemployment rates with current signs of recovery). These various meanings of changing trend categories are indicated during analyses.
4.1.3 Database of analysis
Analyses following changes of socio-economic characteristics of inner peripheries are built on datasets presented in Chapter 1.2 of this annex report. The selection of indicators to be used was based on considerations on the available time span and the content of variables. Due to
their cross-sectional character, data on SGI access could not be used here, while business demographics indicators were excluded from these dynamical analyses because they only cover a shorter period from 2008 to 2014. Thus, only demographic, labour market indicators and variables on economic performance and structure were processed into analysis.
Among the pool of variables representing these dimensions, a reduced selection of indicators was selected carefully chosen by considering their potential meaning in interpreting processes associated with peripheralization. Demographic indicators cover: population dynamics, net migration rate and old age dependency rate. Selected labour market indicators represent processes of temporary (unemployment rate) or temporary exclusion (inactivity rate) from the labour market. Variables of economic performance (GDP per inhabitants) and economic structure (share of employment in manufacturing industry) are also included in these analyses. Countries with no data for several of the selected indicators were excluded from analyses. These countries are: Albania, Liechtenstein, Montenegro, Turkey, Bosnia and Herzegovina, Serbia and Kosovo under UN Security Council Resolution 1244.