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 that contribute 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
gt
gt
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
Percentage of the national population living in
predominantly rural regions close to a city and
predominantly remote rural, 2009
Annual growth rate of population in predominantly
rural regions close to a city (PRC) and
predominantly remote rural (PRR),
1995-2009
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 methodology
Percentage point change in the share of
population living in predominantly urban regions,
1995-2009
17
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.

18
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

19
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/i
nfo/data/esa95/en/titelen.htm
20
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
21
Measure of economic fragility
GDP per capita (national average100) NUTS3
level 2004
22
OECD definition
Gini index of inequality of GDP per capita
acrossTL3 regions, 1995 and 2007
23
Ginis 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
24
Ginis 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.

25
Ginis Heterogeneity Coefficient
Lets 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 Ginis index.
Source OECD, 2011
26
Ginis Heterogeneity Coefficient
I STEP. Sort the dataset from the lowest value
of the variable GDP to the highest one.
27
Ginis Heterogeneity Coefficient
II STEP. Assign a rank to the provinces (items)
according to the order assigned by the previous
step.
28
Ginis Heterogeneity Coefficient
III STEP. Calculate the cumulate intensity of
the variable y, that is
29
Ginis Heterogeneity Coefficient
IV STEP. Calculate the relative frequency
30
Ginis Heterogeneity Coefficient
V STEP. Calculate the relative intensity
31
Ginis Heterogeneity Coefficient
VI STEP. Calculate the difference
32
Ginis Heterogeneity Coefficient
VII STEP. Calculate the sum of the N-1
parameters of Fi and Fi-Qi
33
Ginis 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.
34
Ginis Heterogeneity Coefficient
Another way to calculate the Ginis index based
on the average difference
I STEP. Calculate the difference
35
Ginis Heterogeneity Coefficient
Another way to calculate the Ginis index based
on the average difference
II STEP. Calculate the average of the differences
calculated in the previous step
36
Ginis Heterogeneity Coefficient
Another way to calculate the Ginis index based
on the average difference
III STEP. Calculate the average of the variable
of interest (in our case GDP per capita)
37
Ginis Heterogeneity Coefficient
Another way to calculate the Ginis index based
on the average difference
IV STEP. Apply the following ratio to calculate
the Ginis index
38
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.

39
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.
40
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
41
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
42
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.
43
OECD definition
Correlation coefficient between the percentage of
labour force with tertiary education and the
population share by regional type, 2008 (TL2)
44
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.
45
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 Pearsons correlation (before 1900),
the famous Pearsons Product Moment Sample
Correlation Coefficient generally indicated with
the letter r. The Spearmans 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 Pearsons index r by which it
is derived. The Spearmans index can vary from -1
to 1, like r. ? -1 ? maximum negative
correlation ? 1 ? maximum positive
correlation ? 0 ? No correlation
46
Spearman Correlation Index
The measure of the correlation according to the
Spearmans 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 Spearmans 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)
47
Spearman Correlation Index
We can divide the index ? into 7 steps. Lets
introduce the following example (Soliani,
2003) FIRST STEP define the couples of
observed variables
48
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.
49
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.
50
Spearman Correlation Index

• FOURTH STEP Calculate the Pearsons 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
51
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
52
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 r1, 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 r0, if X is increasing sorted and Y is
  randomly distribute, then D ? average value
  depending from the number of observations

53
Spearman Correlation Index
SIXTH STEP Calculate the Spearmans Correlation
Index The Spearmans correlation coefficient is
derived from the Pearsons index and it can be
written as
The Spearmans coefficient is simply the
Pearsons correlation coefficients applied to the
ranks. A simpler formulation of the index that
use the Hotelling-Pabst test is
54
Spearman Correlation Index
55
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.
56
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 regions 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).

57
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
58
Rurality

• 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.

59
Rurality

• 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.

60
Rurality

• 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.

61
Typology of Rural Areas
Specialized agricultural areas
HIGH
Segmented areas
Dreamland
Quantitative relevance of agriculture
New rural areas
Peripheral areas
Suburbia
LOW
REGRESSION
DEVELOPMENT
Van der Ploeg et al., 2008
62
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).
63
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

64
Development strategies

• All the information that can be obtained from
  regional databases should be elaborated and
  analysed in order to formulate a strategy that
  can consider local specificity of each region.
• Some recommendations
• Think global and act local.
• Improve the capacity of local actors.
• Strengthen the co-operation of local actors.
• Try to affect the balance of power in external
  networks.
• Adjust administrative structures.
• Use a comprehensive territorial development plan.
• Agglomeration processes are the key factor for
  economic growth.
• Challenges over the medium and long term.

65
OECD Members
OECD Member Countries
BRICS Countries
66
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).
67
NUTS
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.
68
NUTS
69
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.
70
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.
71
Von Thünen Model
Lets 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
72
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
73
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
Rent function of the land
d distance
74
Von Thünen Model
The production of the three crops will be
spatially distributed according to the concentric
circles resulting from the total rent
maximization.