The Concept of Rurality

1. The Concept of Rurality

2. 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

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

4. Rurality 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”.

5. Rurality • 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.

6. 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.

7. 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. http://www.ers.usda.gov/Briefing/Rurality/Typology/

8. 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.

9. 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.

10. OECD methodology > >

11. 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

12. OECD methodology North America + Chile Europe (OECD Countries)

13. OECD methodology Distribution of population and area into predominantly urban (PU), intermediate (IN) and predominantly rural (PR) regions, 2009 Population

14. 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

15. 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

16. 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.

17. 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

18. 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). http://circa.europa.eu/irc/dsis/nfaccount/info/data/esa95/en/titelen.htm

19. 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

20. Measure of economic fragility GDP per capita (national average=100) NUTS3 level 2004

21. OECD definition Gini index of inequality of GDP per capita acrossTL3 regions, 1995 and 2007

22. 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:

23. 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.

24. 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

25. Gini’s Heterogeneity Coefficient I STEP. Sort the dataset from the lowest value of the variable GDP to the highest one.

26. Gini’s Heterogeneity Coefficient II STEP. Assign a rank to the provinces (items) according to the order assigned by the previous step.

27. Gini’s Heterogeneity Coefficient III STEP. Calculate the cumulate intensity of the variable y, that is:

28. Gini’s Heterogeneity Coefficient IV STEP. Calculate the relative frequency:

29. Gini’s Heterogeneity Coefficient V STEP. Calculate the relative intensity:

30. Gini’s Heterogeneity Coefficient VI STEP. Calculate the difference :

31. Gini’s Heterogeneity Coefficient VII STEP. Calculate the sum of the N-1 parameters of Fi and Fi-Qi

32. Gini’s Heterogeneity Coefficient VIII STEP. 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.

33. Gini’s Heterogeneity Coefficient Another way to calculate the Gini’s index based on the average difference I STEP. Calculate the difference:

34. Gini’s Heterogeneity Coefficient Another way to calculate the Gini’s index based on the average difference II STEP. Calculate the average of the differences calculated in the previous step:

35. Gini’s Heterogeneity Coefficient Another way to calculate the Gini’s index based on the average difference III STEP. Calculate the average of the variable of interest (in our case GDP per capita):

36. Gini’s Heterogeneity Coefficient Another way to calculate the Gini’s index based on the average difference IV STEP. Apply the following ratio to calculate the Gini’s index:

37. 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.

38. 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.

39. 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

40. 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

41. 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.

42. OECD definition Correlation coefficient between the percentage of labour force with tertiary education and the population share by regional type, 2008 (TL2)

43. 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.

44. 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

45. 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)

46. 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

47. 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.

48. 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.

49. 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

50. 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: