The Concept of Rurality
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The Concept of Rurality. Weight of Rural. According to the OECD definition of rural, More than 75% of the OECD land area is predominantly rural Where 25% of the entire population lives

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The Concept of Rurality

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The Concept of Rurality

Weight of Rural

According to the OECD definition of rural,

More than 75% of the OECD land area is predominantly rural

Where 25% of the entire population lives

The main economic activity of these areas is agriculture thatcontribute very few to the Gross Value Added (GVA) of the OECD group of countries.

In EU, in 2007, the contribution of agriculture to GVA was of 1,4%

Agriculture can no longer be considered the backbone of the rural economy

Weight of Rural

Contribution of Agriculture to Gross Value Added by NUTS 2 regions, 2007 (in %)


The concepts of “rural” and “rurality” are very difficult to define and different ideologies have shaped the different definitions and rural-urban relationships.

A variety of models has been developed trying to explain why the economic activity concentrates in some regions and areas, especially towns. These models have hierarchical vision of space and tend to see the rural world dependent on the town.

Other approaches is based on the theory of the “district” (Becattini, 1987; Sforzi, 1987) and on the idea that the conditions of success of an economic activity is linked to the specific characteristics of the local economy and society.

There is no common definition of rurality of rural areas. According to European Commission (2006),

“the complexity of a common definition is related to the various perceptions of those elements that characterize "rurality”, the difficulty to collect relevant data at the basic geographical units level and to the need to have a tailor-made definition according to the "object“ being analyzed or policy concerned”.


  • For decades, there has been a wide and persistent belief that rural regions are synonymous of decline. Why? For many reasons:

  • Rural regions have a relatively large but shrinking agricultural sector compared with urban regions.

  • Rural regions lack advantages of agglomeration and economies of scale that characterize metropolitan areas, resulting in higher unit and transaction costs form public, consumer and business services in these areas.

  • Many rural regions are not well connected to the transport and communication networks linking major urban nodes, which are critical sources of information, innovation, technology and finance.

  • These disadvantages may results in a scarcity of economic opportunities, especially high-paying jobs, relatively low per-capita incomes, declining levels of public services, out-migration process.

  • As a consequence, declining and ageing rural populations may threaten the rural regions.

Rural-Urban Continuum

For many decades rural regions have been understood on the basis of the classical rural-urban continuum paradigm.

This approach was developed in USA in the 50s (Dewey, 1960).

In this perspective, the urban and the rural are polar opposites along one singular dimension in which more urban translates into less rural and vice versa.

This approach was adopted to overcome the dichotomy between town and countryside and trace a conjunction between them.

The OECD methodology to categorize rural areas can be considered an extension of the urban-rural continuum paradigm.

In USA, the Economic Research Service of the USDA uses the so-called rural-urban continuum codes to classify the counties.

ERS-USDA R-U Continuum Codes

Rural-Urban Continuum Codes is a classification scheme that distinguishes metropolitan (metro) counties by the population size of their metro area, and nonmetropolitan (nonmetro) counties by degree of urbanization and adjacency to a metro area or areas.

The metro and nonmetro categories have been subdivided into three metro and six nonmetro groupings, resulting in a nine-part county codification.

The codes allow researchers working with county data to break such data into finer residential groups beyond a simple metro-nonmetro dichotomy, particularly for the analysis of trends in nonmetro areas that may be related to degree of rurality and metro proximity.

These scheme defines the regional typologies.

OECD Approach

The OECD Approach to define the concept of “rural” is based on three dimensions:

A. Spatial dimension (territory), that considers different situation at territorial level in relation to the development tendencies.

B. Multivariable approach. At the same time, demographic, social, economic and environmental aspects are considered. This allows to consider the possible interactions among different variables characterising rural regions with important implications in terms of policy definition

C. Dynamism. The analysis does not capture the picture of a certain moment but also the evolution of each variable.

Although OECD approach is widely adopted and relative “easy” to implement, in literature it is possible to find some criticisms addressed to the methodology.

OECD methodology

  • The OECD has established a regional typology to which regions have been classified as predominantly urban (PU), predominantly rural (PR) and intermediate rural (IR) adopting the following 3 criteria:

  • Population density: a community is defined as rural if its population density is below 150 inhabitants per km2 (500 inhabitants for Japan).

  • Percentage of population in rural areas: a region is classified as

    • predominantly rural if more than 50% of its population lives in rural areas,

    • predominantly urban if less than 15% lives in rural areas and

    • intermediate if the share is between 15% and 50%.

  • Urban centres: a region that would be classified as rural on the basis of the general rule is classified as intermediate if it is has an urban centre of more than 200.000 inhabitants (500.000 for Japan) representing no less than 25% of the regional population; on the other hand, if a region is classified as intermediate rural but it has an urban centre of more than 500.000 inhabitants (1 mln for Japan), then it is classified as urban.

OECD methodology



OECD methodology

Urban-Rural typologies at NUTS3 level

Predominantly Urban regions

Intermediate rural regions, close to the city

Intermediate rural regions, remote

Predominantly rural regions, close to the city

Predominantly rural regions, remote

No Data

Source: European Commission, DG Regional Policy

OECD methodology

North America + Chile

Europe (OECD Countries)

OECD methodology

Distribution of population and area into predominantly urban (PU), intermediate (IN) and predominantly rural (PR) regions, 2009


OECD methodology

Annual growth rate of population in predominantly rural regions close to a city (PRC) and predominantly remote rural (PRR), 1995-2009

Percentage of the national population living in predominantly rural regions close to a city and predominantly remote rural, 2009

OECD methodology

Share of population living in predominantly rural (PR), intermediate (IN) or predominantly urban regions (PU) in 2009 and millions of new urban dwellers: OECD countries, Brazil, South Africa, China and India, 2000-2009

OECD approach

  • Rural regions have low population densities and are located in areas where there are not major urban centre.

  • Low population densities and relative remoteness give rise to a range of problems that have impact on economic activity and individual well-being.

  • In general terms, this situation can engender disparities between rural and urban regions.

  • The factors that contribute to the fragility of rural regions are:

    • Out of migration and ageing;

    • Low educational attainment;

    • Lower average labour productivity;

    • Low level of public services.

Measure of economic fragility

  • One of the most important measure of the regional fragility is the Gross Domestic Product (GDP) per capita.

  • GDP is a basic measure of a country's overall economic health.

  • GDP is equal to the sum of the gross value-added of all resident institutional units (i.e. industries) engaged in production, plus any taxes, and minus any subsidies.

  • GDP is also equal to the sum of the final uses of goods and services (all uses except intermediate consumption) measured in purchasers' prices, minus the value of imports of goods and services

  • GDP is finally equal to the sum of primary incomes distributed by resident producer units.

  • In fact, GDP can be defined in three ways:

  • Output approach

  • Expenditure approach

  • Income approach

Measure of economic fragility

a. Output approach - GDP is the sum of gross value added of the various institutional sectors or the various industries plus taxes and less subsidies on products (which are not allocated to sectors and industries).

b. Expenditure approach - GDP is the sum of final uses of goods and services by resident institutional units (final consumption expenditure and gross capital formation), plus exports and minus imports of goods and services.

c. Income approach - GDP is the sum of uses in the total economy generation of income account: compensation of employees, taxes on production and imports less subsidies, gross operating surplus and mixed income of the total economy.

The concept is used in the European System of Accounts. GDP at market prices is the final result of the production activity of resident producer units (ESA 1995, 8.89).

Measure of economic fragility

Percent age of TL3 regions with GDP per capita below OECD average and GDP growth rate by typology of region, 1995-2007

Measure of economic fragility

GDP per capita (national average=100)

NUTS3 level


OECD definition

Gini index of inequality of GDP per capita acrossTL3 regions, 1995 and 2007

Gini’s Heterogeneity Coefficient

The Gini index is a measure of inequality among all regions of a given country.

The index takes on values between 0 and 1, with zero interpreted as no disparity.

It assigns equal weight to each region regardless of its size; therefore differences in the values of the index among countries may be partially due to differences in the average size of regions in each country.

In OECD studies, regional disparities are measured by an unweighted Gini index. The index is defined as:

Gini’s Heterogeneity Coefficient

  • N is the number of regions

  • , the relative frequency

  • yi is the value of the variable considered (GDP per capita, …) ranked from lowest (y1) to the highest (yN) value.

Gini’s Heterogeneity Coefficient

Let’s consider the data about the GDP per capita of Belgium for a certain year. We want to calculate the level of disparity between the different areas in Belgium using the Gini’s index.

Source: OECD, 2011

Gini’s Heterogeneity Coefficient


Sort the dataset from the lowest value of the variable GDP to the highest one.

Gini’s Heterogeneity Coefficient


Assign a rank to the provinces (items) according to the order assigned by the previous step.

Gini’s Heterogeneity Coefficient


Calculate the cumulate intensity of the variable y, that is:

Gini’s Heterogeneity Coefficient


Calculate the relative frequency:

Gini’s Heterogeneity Coefficient


Calculate the relative intensity:

Gini’s Heterogeneity Coefficient


Calculate the difference :

Gini’s Heterogeneity Coefficient


Calculate the sum of the N-1 parameters of Fi and Fi-Qi

Gini’s Heterogeneity Coefficient


Calculate the ratio:

The results indicates a low heterogeneity within the Belgian region. This means that the GDP per capita is very similar in each region.

Gini’s Heterogeneity Coefficient

Another way to calculate the Gini’s index based on the average difference


Calculate the difference:

Gini’s Heterogeneity Coefficient

Another way to calculate the Gini’s index based on the average difference


Calculate the average of the differences calculated in the previous step:

Gini’s Heterogeneity Coefficient

Another way to calculate the Gini’s index based on the average difference


Calculate the average of the variable of interest (in our case GDP per capita):

Gini’s Heterogeneity Coefficient

Another way to calculate the Gini’s index based on the average difference


Apply the following ratio to calculate the Gini’s index:

OECD approach

  • Rural regions have low population densities and are located in areas where there are not major urban centre.

  • Low population densities and relative remoteness give rise to a range of problems that have impact on economic activity and individual well-being.

  • In general terms, this situation can engender disparities between rural and urban regions.

  • The factors that contribute to the fragility of rural regions are:

    • Out of migration and ageing;

    • Low educational attainment;

    • Lower average labour productivity;

    • Low level of public services.

OECD approach

Out of migration and ageing.

Rural regions are increasing dependent on in-migration to maintain population levels and labour force.

For a long time, rural regions had positive natural balances and were net exporters of population to urban regions. This situation is changed considerably losing population.

Younger residents abandon rural areas to move towards urban centres.

Although this is generally true, the extent of ageing in rural regions varies greatly across and within countries.

OECD approach

Distribution of the elderly population in predominantly urban (PU), intermediate (IN) and predominantly rural (PR) regions, 2008

Elderly dependency rate: Country average and in predominantly urban and predominantly rural regions, 2008

OECD approach

The regional elderly population is the regional population of 65 years of age and over.

The elderly dependency rate is defined as the ratio between the elderly population and the working age (15-64 years) population.

Population over 65 years

Elderly Dependency Rate (EDR) =

X 100

Population between 15 - 64 years

OECD approach

Educational attainment.

The general pattern in most OECD countries is that the percentage of the population attending school up to secondary education is typically around or often above the national average in predominantly rural areas.

The percentage of the rural population with tertiary education in all OECD countries is lower than the national average.

The rural people in rural areas attends school like other people in other regional areas up to secondary level and then leave the region to pursue tertiary education and find employment outside their home region.

OECD definition

Correlation coefficient between the percentage

of labour force with tertiary education and the population

share by regional type, 2008 (TL2)

Spearman Correlation Index

The Spearman correlation coefficient is a measure of association between two variables to test whether the two variables covary, that is to say whether as one increases the other tends to increase or decrease.

The two variables are converted to ranks and a correlation analysis is done on the ranks.

The Spearman correlation coefficient varies between –1 and 1 and the significance of this is tested in the same way as for a regular correlation.

The Spearman correlation coefficient measures the strength and direction of the relationship between two variables.

In our case, the labour force with advanced educational qualifications and the share of population in predominantly urban (PU), intermediate (IN) or predominantly rural (PR) regions. A value close to zero means no relationship.

Spearman Correlation Index

  • The method was proposed in 1904 by C. Spearman with the paper “The proof and measurement of association between two things”, American Journal of Psychology vol. 15, pp. 72 – 101.

  • The method is a correlation based on the Ranks and it is based on the Pearson’s correlation (before 1900), the famous Pearson’s Product Moment Sample Correlation Coefficient generally indicated with the letter r.

  • The Spearman’s index is generally indicated with the Greek letter  (rho), or in some cases with the symbol rs in order to trace a relation with the Pearson’s index r by which it is derived.

  • The Spearman’s index can vary from -1 to +1, like r.

    •  = -1  maximum negative correlation

    •  = +1  maximum positive correlation

    •  = 0  No correlation

Spearman Correlation Index

The measure of the correlation according to the Spearman’s index is calculated in relation with a couple of variables, X and Y.

The variables X and Y must be sortable, in the sense that for each variable it is possible to make an order of each item.

To apply Spearman’s index, the null hypothesis (H0)of independence between X and Y should be verified; in other terms, it is necessary to verify that the probability that the N values of X can be associated to the N values of Y is the same.

The alternative hypothesis (H1) that an association between X and Y exists can provide:

Positive result: direct association  if X is high (low), Y is high (low)

Negative result: indirect association  if X is low (high), Y is high (low)

Spearman Correlation Index

We can divide the index  into 7 steps. Let’s introduce the following example (Soliani, 2003):

FIRST STEP : define the couples of observed variables

Spearman Correlation Index

SECOND STEP : sort the rank of the variables

In this step, it is necessary to sort the variable X in such a way that the smallest value compare in the first position and the highest value in the last position. Each observed value is substituted by the position number (integer value). If there are same values of X calculate the average of their ranks.

The observed values for Y must be shifted according to the X sorting.

Spearman Correlation Index

THIRD STEP : Ranks of Y

Substitute the rank of each value in Y inside the table. If there are same values of Y calculate the average their rank.

Spearman Correlation Index

  • FOURTH STEP : Calculate the Pearson’s Correlation

  • Considering the observed values associated to the two variables, se can say that:

  • If r = +1 , the two variables are positively correlated (the value of X and Y for each subject is the same);

  • If r = -1, two variables are negatively correlated (the highest values of X are associated to the lowest values of Y, and vice versa);

  • If r = 0, the two variables are not correlated (the values for X and Y are distributed randomly);

  • In the example:

r = 0,79

Spearman Correlation Index

FIFTH STEP : Calculate the Hotelling-Pabst Test (measure of correlation)

To quantify the degree of correlation between two variables Spearman proposed to calculate the distance within each couple of ranks, as follow:

Spearman Correlation Index

FIFTH STEP : Calculate the Hotelling-Pabst Test (measure of correlation)

We can now calculate the Hotelling-Pabst Test in the following way:

  • When r=+1, the couple of observations X and Y have the same rank and, thus, D = 0

  • When r=-1, if X is increasing sorted and Y is decreasing sorted, then D = MAX (depending to the number of couples)

  • When r=0, if X is increasing sorted and Y is randomly distribute, then D  average value depending from the number of observations

Spearman Correlation Index

SIXTH STEP : Calculate the Spearman’s Correlation Index

The Spearman’s correlation coefficient is derived from the Pearson’s index and it can be written as:

The Spearman’s coefficient is simply the Pearson’s correlation coefficients applied to the ranks. A simpler formulation of the index that use the Hotelling-Pabst test is:

Spearman Correlation Index

Spearman Correlation Index

SEVENTH STEP : Verify the null Hypothesis

The Null hypothesis according which:

H0:  = 0 vs. H1:  ≠ 0

For a probability  prefixed, we refuse the Null hypothesis if the value calculated with t-Student formula with N-2 degrees of freedom if the empirical t is greater than the theoretical t. In this case, we accept the alternative hypothesis.

OECD definition

  • Labour Productivity.

  • The lagging economic performance of rural regions is generally explained by lower average labour productivity. In this context, the labour productivity can be explained using the GDP per worker.

    A lower GDP per worker could be due to a number of factors, like:

    • Specialization in lower value added sectors (agriculture)

    • Lesser educated workforce;

    • Lower percentage of the region’s population in the labour workforce;

    • Higher unemployment rate;

    • Greater percentage of older persons;

    • Higher rate of commuters employed in other regions;

    • Lower average labour productivity (GDP per worker).

OECD definition

Public services.

The demographic structure of rural regions is often not appropriate to support provision of local public services.

This is due to a vicious circle typical of rural areas:


A different approach due to van der Ploeg et al. (2008) does not assume that the rural and urban are mutually exclusive.

The simple divide between rural and urban no longer fits with the spatial, cultural, economic and social characteristics of the actual situation in the world and, in particular, in EU.

Town and countryside are intimately linked and interdependent.

New need in term of more rurality to maintain a balanced society and an acceptable quality of life.

Rural is no longer the antipode of the city, but above all it is a multi-facetted prerequisite.

It is important to identify the relationships between the town and the countryside in terms of needs, benefits obtained by mutual exchanges, but also disadvantages due to land uses, processes of abandonment.


In certain cases, rural areas might be of scarce interest for the cities and citizens.

These areas can be represented by remote areas or, also, specialized agricultural areas.

It is not necessary that food comes from farms near the towns, because nowadays this need can be satisfied by the modern markets.

The classical Von Thünen model in the globalization era in most situation is no longer applicable.

The risk is that these areas will be abandoned or will become a “reservoir”. Nobody will care about them!

In other cases, rural areas compensate lacking services in urban spaces (quietness, landscape, amenity space, animals, etc.).

The agriculture can be perceived by the town as an articulated and multifunctional providers of goods (products and services).

The new urban needs and new rural services interact.

The Von Thünen model can be applied for these latter rural functions.


  • According to van der Ploeg et al. (2008) in the book “Unfolding Webs”, the rural is the place where the ongoing encounter, interaction and mutual transformation (the co-production) of man and living nature is located.

  • This encounter occurs through a wide range of different practices, which are spatially and temporally bounded. These include agriculture, forestry, fishing, hunting, rural tourism, rural sports and living in the countryside.

  • The rural is characterized by particularly institutions (farm households), social relations, traditions, identities, culture.

  • During the past years, the rural has suffered a strong shift within the relationships between man and living nature:

    • Strong decline in many rural areas of the agricultural activity;

    • Rural tourism, rural housing, rural sports have become important new elements of the regional rural economy.

    • In some sectors, farming is separated from living nature.

Typology of Rural Areas

Specialized agricultural areas


Segmented areas


Quantitative relevance of agriculture

New rural areas

Peripheral areas





Van der Ploeg et al., 2008

Typology of Rural Areas

Specialized agricultural areas. Where farming shows a high degrees of specialization, intensity and scale and where other economic sectors are only weakly connected to agriculture (Flavoland, Ile-de-France).

Peripheral areas. Regions where farming never palyed a major role (Finnish woodland) and where agriculture is in decline (South Italy).

New rural areas. Agriculture is developing along the line of multifunctionality and it is interconnected with regional economy and society (Tuscany in Italy).

Segmented areas. Near specialized agricultural activities, other sectors linked to agriculture are developing (rural housing). The Po Valley can represent an example.

Suburbia. Agriculture is declining and new settlement patterns are emerging, with a fundamental role played by commuting (surroundings of Dublin and Rome).

Dreamland. Rural regions that reflects additional and highly contingent tendencies. Place very crowded in some periods of the year (summer) and abandoned in other (winter).

Typology of Rural Areas

  • Rural is often view as the opposite of Urban, so in a negative sense. Urban region is a developed and rich place, while rural region is a declining and poor place.

  • To define objectives and policies to develop rural regions it is important to provide a positive content to the rural regions.

  • Three features to define rural and to analyse the interactions that are linked with rural areas:

  • The rural is the place of co-production between social and the natural, between man and living nature.

  • The rural is characterized by a predominance of small and medium enterprises (SMEs) that sometimes group together in clusters or districts.

  • Within rural areas

OECD Members

OECD Member Countries

BRICS Countries

Gross Value Added (GVA)

GVA at producer prices is output at producer prices minus intermediate consumption at purchaser prices.

The producer price is the amount receivable by the producer from the purchaser for a unit of a product minus value added tax (VAT), or similar deductible tax, invoiced to the purchaser.

The concept is used in the European System of Accounts, Gross Value Added (ESA 1995, 8.11) is the net result of output valued at basic prices less intermediate consumption valued at purchasers' prices. Gross value added is calculated before consumption of fixed capital.

It is equal to the difference between output (ESA 1995, 3.14) and intermediate consumption (ESA 1995, 3.69).


The Nomenclature of territorial units for statistics, abbreviated as NUTS (from the French 'Nomenclature des Unités territoriales statistiques') is a geographical nomenclature subdividing the territory of the European Union (EU) into regions at three different levels (NUTS 1, 2 and 3, respectively, moving from larger to smaller territorial units).

Above NUTS 1 is the 'national' level of the Member State. NUTS areas aim to provide a single and coherent territorial breakdown for the compilation of EU regional statistics.

The current version of NUTS (2006) subdivides the territory of the European Union and its 27 Member States into 97 NUTS 1 regions, 271 NUTS 2 regions and 1303 NUTS 3 regions. The NUTS is based on Regulation 1059/2003 on the establishment of a common classification of territorial units for statistics, approved in 2003 and amended in 2006 by Regulation 105/2007.

At a more detailed level, there are the districts and municipalities. These are called "Local Administrative Units“ (LAU) and are not subject of the NUTS Regulation.


Von Thünen Model

Johann Heinrich von Thünen was a North German landowner form Mecklemberg areas.

Although educated at Göttingen, he spent most of his life managing his rural estate, Tellow.

In his first volume, “The isolated state”, published in 1826, he analyses the spatial economics in relation with the theory of rent.

Von Thünen Model

The hypothesis on which the model is grounded are:

1. A featureless plain, homogenous, where population and infrastructures are equally distributed.

2. One unique market centre, where the agricultural products can be exchanged

3. Inputs are widely available without costs of transport.

4. For each agricultural product it is possible to built a production function

5. The price is defined exogenously

6. The unitary transportation cost is constant.

7. The technology is fixed.

Von Thünen Model

Let’s assume that:

p : agricultural product price (output price)

c : marginal cost of production

x : quantity of output obtained cultivating 1 hectare of land

d : distance from the market place

t : transportation cost for each unit of output

r : rent obtained per 1 hectare of cultivated land

The RENT is the amount of money that remains to the landowner after paying the cost of production and the cost of transport. In other terms:

Von Thünen Model

Assuming that in a certain area three agricultural processes (wheat, tomato, sugarbeet) are produced, we can represent three rent functions, one for each crop:

r: rent

Rent of wheat

Rent of tomato

Rent of sugarbeet

d: distance

Von Thünen Model

The landowners will decide to produce the crops in those places that permits to earn the maximum level of rent. Hence, considering three crops:

r: rent

Rentfunctionof the land

d: distance

Von Thünen Model

The production of the three crops will be spatially distributed according to the concentric circles resulting from the total rent maximization.

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