Economic Geography Notes PDF

ECONOMIC GEOGRAPHY From Wikipedia Economic geography is the study of the location, distribution and spatial organization of economic activities across the world o Represents a traditional subfield of the discipline of geography o However, many economists have also approached the...

ECONOMIC GEOGRAPHY From Wikipedia Economic geography is the study of the location, distribution and spatial organization of economic activities across the world o Represents a traditional subfield of the discipline of geography o However, many economists have also approached the field in ways more typical of the discipline of economics Economic geography has taken a variety of approaches to many different subject matters, ex: location of industries, economies of agglomeration, transportation, international trade, development, real estate, gentrification, ethnic economies, gendered economies, core-periphery theory, the economies of urban form, the relationship between the environment and the economy, and globalization Classical location theories: developed in Germany Development is unevenly distributed at the geographic scale 05/10/23 A goal is to explain why there are economic gaps between people ADAM SMITH: Division of labor is limited by the extent of the market o “There are in Africa none of those great inlets, such as the Baltic and Adriatic seas in Europe, the Mediterranean and [Black Sea] in both Europe and Asia…to carry maritime commerce into the interior parts of that great continent: and the great rivers of Africa are at too great distance form one another to give occasion to any considerable inland navigation” – A. Smith, The Wealth of Nations (1776) o For example, if we consider Africa and Europe, Adam Smith explains that the African rivers are too far from one other to give occasion to any considerable inland navigation → Loss in productivity* = time in between going from one division to another Africa is large and mainly landlocked: the ratio coastline/inland of Europe is 3.5 larger than its value in Africa → The value of GDP is largely explained by landlocky o Coastlines and access to the sea allow for exports, imports, trades and migration, making the size of the market bigger o Hence, Europe grew more than Africa: thereby, Smith didin’t consider colonialization processes in his analysis, but in principle landcoasts are advantaged over landlocked countries 1 → *Idea of specialization as increase in productivity: the more you engage in an activity the more productive it becomes Specialization and Seacoasts and Trade (larger improvement in Growth Navigable rivers markets) knowledge Landlocked Stagnation A bigger market calls for specialization and improvement in knowledge → resulting in an increase in productivity (Geo size of the market Trade Spec Growth) more income to spend → meaning larger demand for goods → increase in the size of the market SO we have a situation where A influences B and vice versa = mutual, positive influence → Positive feedback (circular causation) both positive values positively correlated SO having GE + mkt size + T + S + GR → We have what we call a positive feedback Positive feedback = DIVERGING PHENOMENON: it is something that gets the rich richer and the poor poorer o If positive feedback is happening and you have countries x and y, one country has an advantage in terms of geographic characteristics, due to this the one with the advantage over time will grow and the one which is not will see a decrease in the economic activity → Positive effect is not nice on itself because it determines that what is larger becomes stronger and larger and so on; on the other hand if something is smaller it becomes weaker and so on o If a country is landlocked, it means leading to stagnation, because less trade, less productivity and more stagnation and so on DIAMOND’S HYPOTHESIS (1997) - Similar to the idea of Adam Smith (but also includes negative feedback) The model: Agriculture and other ideas also spread faster in Eurasia, because its major axis is East-West, along a similar latitude (In America or Africa, major axis is North-South) o The east-west axis allows an easy spread of species both vegetable and animals and this led to the domestication of plants and animals which is the basis of the transformation of human group from nomadic life to resident life and beginning of villages o → Food surpluses and food storage → People stay in one place which leads to large, dense, sedentary and stratified societies o In this societies, there is better technology because people become specialized → Between technology and stratified societies there is a positive feedback → Reason for which Europe developed before BUT hard to test this idea!!! We can test this idea through climate change The idea is that there are some outlet factors that go directly to produce growth with their effect, and there are also prospect factors 2 → THE DIFFERENCE BETWEEN ADAM SMITH AND THE DIAMOND’S HYPOTHESIS: the density of people and animals which can eventually lead to a negative feedback o For Example: Covid and Sars and their spread are all related to density, which is why the first reaction was a lockdown Diamond also talks about a negative feedback Negative feedback = DIVERGING DYNAMICS: something in which A positively influences B, but B negatively influences A you get a sort of equilibrium, A is causing B to grow and B is causing A to fall back (= depressing effect) o EX: The more crops and animals are present, the easier it is for epidemic diseases to arise (epidemics also come from trade, ex. the black plague) → Negative feedback which leads to a sort of equilibrium → Prevails upon positive feedback Graph of Diamond’s Hypothesis 3 10/10/2023 THE GRAVITY MODEL Land is a surface in which economic activity can be performed: For centuries, geography was not present in the books of economics More recent models like this one highlight a way in which space and geography enter into the description of the economic model: the GRAVITY MODEL (created in the 60s) → Empirical evidence may explain the amount of trades of two countries by an equation that has to do with physics Space = distance between two points and distance = transportation (transportation costs) The idea is that the existence of space can be mapped as an economic element in terms of distance o Activities are allocated in different spaces, hence separated by space and in need of transportation to move goods analogy with the gravity concept in physics → The bigger the mass (GDP of countries) and the smaller the distance, the bigger the attraction = Negative parameter on the distance → SPACE = DISTANCE = TRANSPORT COSTS THE GRAVITY EQUATION describing trade (or exports) between countries a and b Trade between country a and b is a function of: Economic size of country a, economic size of country b, and distance between the two countries → Distance implies transportation costs! o There is an inverse proportion between transportation costs and the economic size of a country To be able to check the correspondences between model and reality, we need to derive from a simple function a multiplicative function o Because if we choose a product instead of a sum, the factors are imperfectly substitutes (if they were perfectly substitutes, we would have a linear function) ZIPF’S LAW (notes on notebook) - SUMMARY We can consider the size distribution of cities and by ordering them by size The larger is the city the lower is the number of cities of that size: This can be approximated in a straight line and it is called the Zipf’s law o If a city is above or below the line then there is usually an explanation for this ZIPF’S LAW = term used to refer to the idea that city sizes follow a pareto distribution (mathematical formula describing the inequalities of wealth distribution “80-20 rule”), and that the distribution has a shape parameter equal to 1 → Both the OLS Estimator and the Hill Estimator reject Zipf’s law prediction of pareto exponent equal to 1 OLS Method: rejects Zipf’s law prediction of pareto exponent equal to 1 o AND ALSO: States that Zipf’s law also fails for urban agglomerations (lower than 1 = more unequal) → Interpretation: growth of cities in the past 20 years mainly took the form of suburbanization (= increasing inequalities between urban agglomerations) Hill Estimator: rejects Zipf’s law prediction of pareto exponent equal to 1, BUT for a minority of countries than the OLS → Several explanations for variations in the value of the pareto exponent 4 We need to look at how reality is through the stylized fact and then we can consider the theories and explain why reality is how we observe it WHAT DO WE KNOW ABOUT CLUSTERING? When observing spatial distribution of a phenomenon (ex. location of firms) in a given territory (nation or region), three main structures can emerge: o Clustering: most firms tend to concentrate in a single or few locations o Avoidance: all firms tend to be uniformly scattered in order to maximize inter-firm distances o Independence: no clear spatial pattern is visible, locations are determined by a random process → There is a specific branch of statistics (statistical analysis f spatial point patterns) which studies specific patterns of distributions of events over a plane, against the null hypothesis of “spatial randomness” → From an empirical point of view, firms generally do cluster: often according to industry o Hight-tech industries are more likely to cluster than other types of firms, because of the relevance that agglomeration economies and knowledge spillovers play in these industries Cluster: the process by which economic activities tend to concentrate in a specific area o Agglomeration and competition are possible explanations for clustering DATA: are studied according to the geographical level of aggregation o Spatial definition: Exogenous, endogenous, MAUP (Moveable Area Unit Problems = when you present a kind of description for what comes next – indicators – you want to make sure to look at the geographical and industrial classification levels at which the indicator is computed)* ▪ In practice when we limit the analysis to standard administrative entities for which data are available (ex. regions or provinces in Italy, counties or regions in UK), BUT difficult to find such homogenous regions in practice o Industrial definition: definition of an industrial cluster per se, definition of what high-tech industries are and international comparability of different countries’ industrial classifications, statistical standards and data collecting procedures o Variable measured: number of firms (legal entity)/establishments (productive unit), measurement of employments, sales in a given area or a given industry ▪ Consider that data are not collected at a regional level but comes from the finance offices, so bias might occur → HOW data are clustered *Movable area unit problem telling us that each and every time you are computing one of the indicators it’s important to know and to be precise about which level of geography and industry you are talking about HAVING ROUGH DATA, WHICH INDICATORS DO WE USE TO EXPLAIN THE CLUSTERING PHENOMENON? INDICATORS: o Simple counts, percentage and maps o Absolute vs Relative measures o Specialization vs concentration 5 WHY DOES THE CLUSTERING PHENOMENON OCCUR? THEORIES – one or more models are included in the theories o CLASSICAL LOCATION THEORY o General equilibrium models o Strategic interactions o NEG: New Economical Geographic Approach o Industrial geography (based in Southern California) o Porter’s competitive advantage o Jacobs urban variety o Diffusion theories o Path dependence (what you observe in a certain moment and specific place is linked to what happens before – Matthew Effect = Tiny historical accidents may produce huge final outcomes) o Biological/ecological models (one of the most efficient for the interaction inside and across firms) Small parenthesis →Efficiency of model: models are simpler versions of reality. One of the principle when representing a model is KISS: keep it simple stupid. The main point is: how much simple? Which elements should be included in the model and which ones shall be excluded? The general rule is: to be able to explain a complex phenomenon with the lowest number of variables. There is a trade- off: simple but not too simple. It is necessary that the model is able to absorb exogenous shocks that were not present in the first formulation of the model The first difference that we have to know about data is whether we use as a geographical definition of what we call a region (subnational unit) or whether we want to use a spatial definition called exogenous or endogenous o In principle despite the fact, we would like to use endogenous definition for regions, we use an exogenous definition the remaining of some historical reason, and the economy has radically changed → Endogenous region > Exogenous region, according to data collected by administrative regions/divisions DATA/INDICATORS i = industry; I = sum of all industries/manufacturing r = region; R = sum of all regions/nation; N = number of regions SIMPLE COUNTS, PERCENTAGE AND MAPS Simple counts: Xir: In general a phenomenon we want to measure is represented by X (firms, employers, sales) o Once we get a figure of the data (ex. we have 100 firms producing cars in a specific area), we need to do the comparison with the national average to see if it’s a cluster of firms or not Comparison with the national average: Xir = Xir/N Local percentage (of the national industry total) of a given industry: AXir = Xir/XiR → Local percentage is computed to have a share of the car manufacturing in that region → Local Percentage measures the relative distribution of an industry in different regions 6 However, sometimes the distribution of the percentages of establishments and employment towards certain locations is biased, simply because some areas are larger or more densely populated than others → Need to solve the size-bias problem ABSOLUTE MEASURES VS RELATIVE MEASURES The first way to solve the size-bias problem, is to weigh the number of firms or the level of regional employment (clustering in larger and densely populated areas is no longer overestimated) Population: PXir = Xir/Pr → BUT This indicator is biased, because working age is different in each country and industrially developed areas are more likely to have a higher population density (labor market pull phenomenon) Total employment: MXir = Xir/MIr IXir = Xir/XIr o Its denominator gives a finer definition of total employment o Weighs the local amount of manufacturing taking the ‘manufacturing size’ of the region into account, avoiding underestimation of particular locations → These indicators calculate the ‘ABSOLUTE PRESENCE’ of a given activity: absolute indexes show the localized consistence of a phenomenon → All measures are conditioned by the size of the state The location quotient RELATIVE MEASURE: may help eliminating size related biases, but run the risk of overestimating specialization in case of negligible absolute relevance This formula is the ratio of a ratio – location quotient is the same, BUT: o The ‘r’ in the numerator refers to the share of a given industry ‘i’ to the total industry ‘I’, within the same region o Instead, the ‘R’ in the denominator refers to the whole state (national level) → Measures how much a given industry ‘i’ counts (= how large is the share of a given industry) in the total economic activity ‘I’ at the national level ‘R’ → The location quotient is a ratio of the share of a given industry ‘i’ in the region and the share of the same industry at the national level = How important is the firm in the region → Measure of relative specialization of region r in industry I with respect to a larger area R (ex. nation) and the total of manufacturing industries I If the numerator is bigger than the denominator = region has an advantage on the national average (region would be much more specialized I that industry, compared to the national situation) If the numerator is smaller than the denominator = region ‘r’ in a given industry ‘i’ is less specialized than the same thing done at the national level ‘R’ → Since I am comparing the region to the country, I can take into account the services, transport etc. → IF quotient value is greater than 1, then local economy is more specialized → IF quotient less than 1, I can think about a policy to improve the situation When looking at the location quotient we need to be careful about the size of a region: If manufacturing of a region is low, the denominator in our formula will be small → By consequence, the result will be higher 7 What is X? What I need to measure? – We want to define which industries are more specialized in this cluster of industries Specialist creatives (= people working in creative jobs in creative industries, ex. copyright in editor firms or people working in the communication department in a steel industry or people working in advertising agencies in the accountancy sector) Total creative workforce = Total employment in creative occupations + Total employment in creative industries If we measure total employment in creative occupations, we will have people researching in chemistry industry, for example Location Quotient: Example about CCI (Creative and Cultural Industries) You see that despite a higher employment in CCI in France, in the UK there is much more creative industry (underestimation of creative employment by just using data from official statistics) BUT economic activity is not evenly distributed → Inequality IF we want to calculate a policy target which deals with the specialization of the production of a country, it is better to calculate the relative specialization of a state and compare it with the relative specialization of another state The study of industrial clustering is similar to the analysis of the degree concentration of an industry → The ideal index of Spatial Concentration (SC) should be a function of: o Number of areas N (where the studied phenomenon is present) o Size inequalities Q of the phenomenon across the areas → Natural to assume that the smaller the number of areas where a given industry is resent, the more unequal the distribution of the industrial location across these areas, the more spatially concentrated is the industry → Concentration as the weighted sum of area shares Spatial Concentration ratio of a given industry: sum of the shares of industry located in the first n regions (n chosen as a significant threshold) o Measures how much of an industry is concentrated in a given territory (looks at the share in some regions of some industries) o May range from n/N to 1 Herfindhal Index (industrial index – industrial economics): the sum of the squares of the share of industry I in a given region o All market shares – squared – summed 8  o If an industry is equally distributed among all the areas, then SHi = 1/N o It ranges from 1 (maximum = unequal geographical distribution of the industry → Concentrated, locational monopoly) to 1/N (minimum value = uniform distribution) WHEN YOU NEED TO COMPARE DIFFERENT INDEXES (for representative purposes) 1) You take the spatial index 2) You subtract the minimum value (- 1/N) 3) You divide everything by the denominator (N-1/N) SPIEGAZIONI Why does this trick work? IF NOM INDEX = max – min/max – min ??? o Nominator ≠ 0, Denominator = 1 o If I want to look the specific level of relative industrialization with respect to another region MEASURES OF SPATIAL INEQUALITY One of the possible measures we want to use is the measure of inequality: 2 main measures o Spatial Coefficient of variation o Gini Coefficient (usually used to measure income inequality in countries) Spatial coefficient of variation (for a given industry): ratio between the standard deviation (always positive value) divided by the average (mean) of the distribution of the same phenomenon o SCVi =  ir/Xir o SCVi ≥ 0 → IF value of SCV is equal to 0: industry is evenly distributed across areas → IF high value of SCV: unequal spatial distribution of the industry in a given territory Gini coefficient: telling us about inequalities relative to the spatial distribution of specific industries in given countries (modified since it originally describes the degree of income inequality within a country) o It is calculated as the ratio of two areas o G = A / (A+B) = 2A = 1 – 2B ▪ Since the area of the triangle (A+B) is by construction equal to 1/2 If industries are concentrated in a specific point, it is possible to use the following formula By definition, the total manufacturing and industry both start at 0 and end at 1 9  If industry i is distributed across the country the same way the share of manufacturing is distributed, I would get a 45º line o If this does not happen, the second line (representing the cumulative share of a given industry compared to the cumulative share of the manufacturing) would be different, BUT it would still start from 0 and arrive at 1 o If the second line is far away from the 45º line → The distribution of the industry is very different from the distribution of manufacturing → High Gini coefficient Area A = Difference between the cumulative share of manufacturing and the cumulative share of industry i (ranking all regions from the region in which the share of industry i is larger and then adding the share of industry i in the second region and in the third region and so on and you’ll get the broken line in this way) → The larger the area A (the larger the distance between the two lines), i.e. the larger the difference between the cumulative share of industry i → The larger is the difference between the spatial distribution of industry i, as compared to the spatial distribution of total industry (in this case manufacturing) o Vice versa: if industry i, is distributed in a country at the regional level exactly on the same pattern of the manufacturing, then the broken line will totally coincide with the 45º line → Area of a will be tiny or equal to 0 Given that 2° (Gini Index) is a square, i twill give us the total surface GRAPH: Cumulative share of manufacturing The larger the Gini Coefficient → The larger the inequality and therefore the larger is going to be the industrial procedure in a given area WHAT DO WE KNOW ABOUT CLUSTERING? CONCENTRATION AND INEQUALITY Normalized Herf.: SNH = SHi – 1/N / N-1/N → By Paul Krugman Krugman-Gini: ½ Xir/XIr – XiR/XIR o considers as uniform a spatial distribution of industry i such that each area registers exactly the same shares for industry i and for total manufacturing I o IF the first term > The second term → Positive difference o IF they are equal: difference = 0 o IF second term > first term: → Positive difference if first term > 0 → Negative difference if first term < 0 Location quotient is similar to Krugman-Gini Index: only difference is that Krugman does not use a ratio but a difference in order to get a positive result SPECIALIZATION VS CONCENTRATION 10  We define REGIONAL SPECIALIZATION as the [distribution of the] shares of an industry in total manufacturing in a specific region j o EX: Lombardy is said to be specialized in the paper industry, if this industry has a high share in the value of total regional manufacturing o The production structure of a region is called “highly specialized”, if a small number of industries is responsible for a large share of the production o Specialization can also be measured for exports, or trade (imports + exports) We define geographic INDUSTRIAL CONCENTRATION as the [distribution of the] shares of regions in an individual industry i o EX: The pulp and paper industry is said to be concentrated, if a large part of production is carried out in a few regions o This interpretation can be applied to various variables (production, exports, trade) → We want to know if an industry is present all over the region, or if it is delocalized in one specific area → These two concepts are not independent from one another: the two things are in fact correlated CONCENTRATION VS. AGGLOMERATION Concentration and agglomeration are both concerned with the question whether or not a specific part of the economic activity can be found at a few locations (a city, regions, countries) Even though concentration and agglomeration are quite similar concepts, as they both deal with the location of economic activity, their foci are quite different FOLLOWING BRÜLHART (1998): CONCENTRATION analyzes the location across space of a few, well defined sectors (notably industries) AGGLOMERATION analyzes the location across space of a much larger part of economic activity, such as the manufacturing sector as a whole (or the total economic activity) o Agglomeration refers to a larger ensemble of industries EU economists at the beginning of POS → Preoccupation – Core-periphery issue 11 Economic policies are mainly based on two drivers 1) Control of concentration and agglomeration (especially tech and development sector) 2) Free movement: migration ease – BUT EU languages do still represent a barrier o EX: Labor movement is very reduced in the EU compared to the US o Labor in certain areas could even become IMMOBILE o EX – Italy: high house ownership = Retaining component 17/10/2023 DEGREE OF REGIONAL SPECIALIZATION 1. CORE-PERIPHERY DYNAMIC: high concentration of industry at the core, leaving the periphery starving o High degree of concentration, low specialisation 2. AUTARCHY: low concentration, one industry, spread evenly in all regions o No specialisation from a regional point of view: Everything is produced regionally, at a lower price, having to deal only with the cost of production o Autarchy is an economy consuming only what is producing 3. INDUSTRIAL DISTRICT: both high specialisation and high concentration o You have a very specialised economy in a region and also a very concentrated industry (“geography and trade”, Krugman) 4. NATURAL RESOURCES: high degree of specialisation, low degree of concentration o Corner can be explained less in economical patterns: Pattern economy driven by nature o An economy largely based on natural resources (African nations), regions endowed with natural resources, but industries are scattered in several regions due to the equality of their natural resources. European politics: looking at the process of concentration of economic activity to foster development and innovation. Second pillar: huge internal migration, despite language barriers. In Italy, old ownership is quite high, and the market for homes is not as large as in the UK. If you own a house, it is already for you to move to a different region, now just imagine moving to a different nation 12 SPECIALIZATION AND CONCENTRATION OVER TIME Focus on the effects of war + unification of Europe with Eastern European countries This graph raises its value as the phenomenon goes down: → Right to left: specialization will increase → Top to down: agglomeration will increase Despite the fear of the European Union, industry concentration went down. Some economist tend to analyse the result of the graphics as the presence of some barriers which still preventing a full integration of economy across country. CONCENTRATION OVER TIME What’s important is to look at the phenomenon at its disaggregated level Period under consideration: same as previous graph ACCORDING TO TAXONOMY PAVITT, IT TENDS TO CLASSIFY INDUSTRIES INTO FOUR CLASSES: 1. Scale intensive: the larger, the more competitive CC Class: industries need to be large in order to be efficient AC will go down as production goes up → Concentration becomes a requirement at the regional level o If firms are large, when placed in a location they will create an agglomeration 2. More heterogeneous group (High-tech): Those industries, especially high tech, have to do that knowledge at the basis of those industries over years has spread across the globe CD Class: The process of European integration has led to some dispersion, in relative terms due to the role of communication technologies that allows distance communication o EX: Beverages and Tobacco industries, are desegregated because they are moving to the East in order to supply new consumers 13  3. Traditional (in the PAVITT taxonomy are those defined as “supplier dominated”): DC Class: technological advances = machineries that are used to make the products which come from outside the country (no new designs of products) They became more dispersed since a larger economy for agglomeration: business near business Since the European process of integration has created a single market, more probability for agglomeration 4. Still dispersed industries (NAT RES): still dispersed even after economic integration DD Class: Industries which rely heavily on natural resources and natural resources are usually not movable, since their weight is high while their value is low → Residual R = all the industries that cannot be classified in the other classes (ex. footwear, medicines, plastic products, iron & steel, manufacturing) SPATIAL SEPARATION INCREASES, NOT LINEARLY If we observe the High Tech industry, we see a decrease of concentration: because of the effects of the communication technology in the high-tech sector o Nowadays it is possible to have research groups placed in different location but keeping in touch through the use of the Internet Speaking of the other firms: the more they are concentrated, the more they’ll be efficient WHY DO FIRMS CLUSTER: LITERATURE’S SURVEY CLASSICAL LOCATION THEORY Born in Germany during the nazi period. These theories survived thanks to an American academic, Walter Isak. Those modes are the first one, and they are very simple 1. LOCALISED SOURCES OF SUPPLY AND DEMAND – (GEOGRAPHICAL AND INSTITUTIONAL FACTORS) WEBER, ISARD, MOSES: supply (input and markets) → Firm’s problem minimizing transport costs o First class of model based on inputs and market: Look at the ideal location of a firm as if only the main determinant of his choice would be the location of inputs and markets o Those models also took other factors for locations into account, but of secondary or third relevance (especially on the location of inputs and markets and their distance) o Main driving engineer: costs → Aim: minimising transport costs. 14  LÖSCH, HOTELLING, CHRISTALLER: look at the demand side → Firm’s problem maximizing market areas (revenues) o What they say is that the main firm problem is to maximise market eras o Here they look at feasible revenues and how to increase them → The first to discuss the problem of strategy in location VON THUNEN: optimal use of the space → Landlord’s problem: maximizing land rents o It makes a switch by underlining, stressing the fact that space is not just a grace factor, not just that need to be covered by distance, it looks at space as an economic input resource, which is used to produce the goods 2. ECONOMIES/DISECONOMIES OF AGGLOMERATION Explicit factors MARSHALL: labor, intermediate inputs, knowledge o There are places in which trade has no mystery and children learn this secret as they are in the air o Knowledge kindly diffused in the area, space is an advantage for knowledge spillover KRUGMAN: used the centripetal forces (as in Marshall) + Centrifugal forces = Consumers dispersion o Centripetal forces from outside to a specific place, but he adds centrifugal forces to balance → Outcome will depend on equilibrium of centripetal and centrifugal forces o The way in which he is able to do so is to assume that households are divided in two: ▪ Agricultural workers and industrial workers that can move → The process of extreme agglomeration will be contrasted by the transportation costs that serve to bring the items to the agricultural workers that cannot move Implicit Factors Specific parents that look simplicity at agglomeration economy: BRIAN ARTHUR: increasing return to location, lock-in phenomena o Dynamic process resolving into a sub-optimum equilibrium o With the increasing return, a location becomes the core of a given industry even if in theory it is not the best location!!! STORPER , WALKER: industries produce regions o Their idea is that regions are not defined by geography, but by industry is BREZIZ - Krugman: technological life cycle of regions o Development of one region is interrelated with what's happening in another region → By looking at these three models we will look at the effect of uncertainty about the optimality of locating in a certain location. EXISTENCE OF UNCERTAINTY, RISK, LIMITED INFO MANSFIELD: technological diffusion HIRHLEIFER, WELCH: informational cascades BANERJEE: herd behaviors → Information about the quality of resources is visible once located, not before 15 LEAST COSTS THEORY: WEBER (1909) Alfred Weber’s book “Theory of the Location of Industries” published in 1909 (translated into English in 1929), founds the least cost approach to industrial location MOST OF THE INPUTS OF PRODUCTION WERE RAW MATERIALS 3 ORIENTATIONS He explains the location of manufacturing firms as arising from the interaction of three factors 1) Transportation costs 2) Labour costs → Regional components (geographically determined) 3) Agglomeration forces: non-regional factor (determined by the interaction of firms’ location decisions 4 MAIN ASSUMPTIONS 1. Location of input is given the inputs of my production are placed in specific locations o Outputs are sold in specific areas as well 2. Size of consumption places (market size) are given + perfect competition for all producers o In those markets, perfect competition for producers' prices does not depend on the scale of production of any producers → firms are price takers → Price is given o The decision to sell or not your products in that market will not influence the price 3. Labour is immobile at several fixed labour location o Those theories were a southern German idea: land is a homogenous land, no mountains; however, you have workers living in small towns because they own their house, therefore imobile workers 4. Unlimited supply of labour at a given wage rate (infinite elasticity of labour supply): Wage rate is fixed o Infinite elasticity means that firms may hire workers without having to increase wages ▪ EX: There is an industrial reservation army in a given location (much more miners than currently employed), so if a new mining firm established in a new village, it can hire workers without having to increase wages, since several unemployed miners are available o Again, the location of several firms that uses workers coming from a given place will not influence the wages. Provide whatever amount of labor is required Weber tried to simplify everything except transport costs: He tried to sterilize any possible variation that would get in the way of describing the model o He assumes that isoquants are L-shaped = specific combinations of labor and capital ISARD+ MOSES introduced substitutability of inputs: Starting from Weber’s theory, they are able to describe a more contemporary story between land and non-land inputs (convex isoquants) → Development of Weber’s approach to industrial location by enlarging its scope and enforcing predictive power o In Moses the optimal location can be calculated by identifying the tangency point between the higher isoquant and the iso-outlay line → There, the ratio of marginal productivity of spatially located production factors = ratio of their delivered price o Multiple location equilibria exist (≠ Weber only one best single location for each type of activity → Solves a conflict between the Weberian location doctrine and reality (numerous examples in which firms in a given industry have different orientations) 16  → This highlighted that perfectly competitive general equilibria (EX. Pareto) cannot be meaningfully applied to spatial economics → ALL HAVE IN COMMON: partial equilibrium analysis, focusing on the supply side of the economy and the driving force of location decision is the minimization of costs (little room for demand variation or strategic interaction between firms) →Take demand as given STEPS OF FINDING OUT THE LEAST COST LOCATION STEP 1: find out the least transport cost site (location where transport costs are at the minimum) between the sources of material inputs and the market for output STEP 2: consider two distorting forces → If the production unit will move to a cheaper labor cost site, and the effect of agglomerative forces/economies SUPPOSE WE HAVE: two raw materials (A and B) + One market (C) → located geographically as shown A. LEAST COST THEORY WEBER: TRANSPORTATION Suppose: two raw materials (A and B) which are inputs, are combined to produce outputs that are sold on market located in C We need to find a point P inside the bounding triangle with the smallest possible associated ‘import’ and ‘export’ costs → Looking for a “center of mass” of the triangle, not the geometric center of mass → Looking for a location where the product of the transport tariffs and the import/export costs are minimized Point P = minimization of this expression Wi= point-specific weight (material & product index) Ti = transport tariff (fee) of material and products Xi = distances to P from material or product location ( A,B,C) → Point P is such that the sum of the three products is the lowest possible Varignon frame: physical analysis to determine the least cost location alternative o A table with 3 holes, weights in the 3 holes as A, B, C o The ring will move in the direction towards the higher value (= smallest possible import and export costs) 17 B. LEAST COSTS THEORY WEBER: LABOUR Then, we look for a location with cheap labor by drawing a series of lines (isodipanes) centered around the point of minimum transport costs L= location of cheap labour Firms move, if and only if, extra costs due to non optimal location are more than compensated by cheap labour costs o Saving in labor costs is equal to the extra cost of increased transportation to new location C. LEAST COST THEORY WEBER: AGGLOMERATION X’= higher agglomeration advantages Agglomerations arise if the isodipanes of a sufficient number of firms intersect o AND if the total output of these firms is greater or equal to the quantity required to benefit from the economies of agglomeration Firms move, if and only if, extra costs due to non optimal location are more than compensated by agglomeration advantages → Yellow area represents this point WEBER: EXTENDED ASSUMPTIONS Presence of an isotropic plan: no geographical differences between places Natural resources are either ubiquitous (located everywhere, therefore they do not matter) or localised → Not true for Middle East countries o Weber is focussing on n outputs located in few places o Weber assumed that water was located everywhere, since he lived in southern Germany Transport system is uniform: realistic assumption if we look at Southern Germany Labour is immobile at fixed points and of different wages: people stay where they are born Markets are at fixed points and demand is infinite: The location of the market is fixed and the demand is infinite, at a given price Perfect competition: price is identical for a given good across market → Unit (average) revenues are fixed o Firms are so small compared to the whole market that the price is given (equal for every firm) → Price-takers o Those markets are perfectly competitive markets Producers’ aim: minimize their costs in order to maximize profits (since revenues are given) Apart from transport cost, labor cost and agglomeration economies, all other factors are not considered (≠ Isard and Moses) 18 THE WEBER LOCATION-PRODUCTION MODEL (OUTCOMES) Location of firms are transfer-oriented: transport cost is the dominant factor in the location decision → The firm chooses the location that minimizes total transport costs Two types of cost: 1) PROCUREMENT COST: cost of transporting raw materials from the input source(s) to the production facility 2) DISTRIBUTION COST: cost of transporting the firm’s output from the production facility to the market(s) → Total Cost (TC) = Procurement cost + Distribution cost → One place where there’s the input and another where’s the market → Cost dependant on weight, and tariff for transport EXAMPLE WITH 1 INPUT AND 1 MARKET TC = cost of moving raw materials to the production unit (procurement cost) + cost of moving the finished output to the market (distribution cost) Searching for the least cost transport site Procurement cost = 0 at the location of raw material (if I locate my firm where raw materials are) Distribution Cost = 0 at the market location → SUM of the two lines will give the total transport cost o In this example, to minimize the total transport cost, I should place my firm near the raw material location Material Index (MI) MI (Material Index) = weight of raw material/ weight of the finished product o Example: 7 tons of sugar beet/ 1 ton of raw sugar = 7 o Example 2: 10 tons of wheat/ 100 tons of beer (due to water being ubiquitous) = 0.1 → MI > 1: weight-loss industry (you lose weight while producing final products), resource-oriented (better to be allocated near source of inputs, as procurement costs are higher) → MI = 1: no weight-gain or weight loss in industry, footloose location – irrelevant position of the firm → MI < 1: weight-gain industry, market oriented (high distribution costs) LOCATION TRIANGLES 19 19/10/2023 NUMERICAL EXAMPLES FOR THE SIMPLEST POSSIBLE CASE: 1 INPUT, 1 OUTPUT 4 ASSUMPTIONS 1. Single transferable output: The firm produces a fixed quantity of a single product, which is transported from the production facility to market M 2. Single transferable input: The firm may use several inputs, but only one input is transported from an input source, F, to the production facility 3. Fixed factor proportions: The firm produces its fixed quantity with fixed amounts of each input o No factor substitution: The technology of production is fixed o In order to produce outputs, you use inputs in a fixed way o All the kink point in isoquants indicate a fixed ratio of inputs 4. Fixed prices: The firm is so small that its production does not affect the prices of its input → The only costs that varies across space is transport cost → The firm will choose that location that minimizes transport costs EXAMPLE: Resource-Oriented firm produces baseball bats Monetary weight input = witi = 10* 1$ = 10$ Monetary weight output = w0t0 = 3 * 2$ = 6$ Distance = x = 10 Consider a firm that has relatively high costs for transporting its inputs Weight losing kind of production → High costs for transporting its inputs (= Procurement Costs) Since there is just one parameter defining the transport rate, this is constant → Means that the transport cost is linear The procurement costs are 0 at R because you locate where the raw material is and the distribution cost is 0 where the market is because you don’t have to bring anything to the market The total cost therefore is 60 at its minimum and 100 to the maximum 20  From this, it is easy to minimize cost by understanding where the minimum cost is and the optimum location is at the minimum point so R EXAMPLE: market-oriented firm producing bottles of beverages Monetary weight input = witi = 1* 1$ = 1$ Monetary weight output = w0t0 = 4 * 1$ = 4$ Distance = x = 10 High costs for transporting output to the market (= Distribution Costs) 21 ADDING REALISM: STEPPED FREIGHT RATE Transport cost may have a different freight rate for different distances (not linear): To work analytically with this idea we consider a line that looks at a nonlinear (concave) freight structure Slope = The cost per tons per mile o It is concave, so it means that as the cost per tons increases the cost per miles decreases → As you move to longer distances, the price will go up In general, this doesn’t change much the rules of the game and this is why Weber assumed that they were linear NON-LINEAR (CONCAVE) FREIGHT STRUCTURE If procurement and distribution costs are concave → The vertical sum of them is another concave curve which has a maximum in an area which is closer to one of the two sides o In the middle still higher values than in corner solutions (even by creating a concave total cost function, still looking at minimizing costs, hence looking at M and R) Decreasing transport rates = corner solution The firm’s location will be once again where raw materials are (never consider an internal solution as total tc would be higher) NON-LINEAR (CONVEX) FREIGHT STRUCTURE Then we consider a situation in which the costs are convex instead of concave If costs are convex: assumed that for longer transportation, the cost per mile increases → No discount for longer transportation compared to short ones The sum of the two convex curves gives a convex curve for which the solution is an interior one → Increasing transport rates: internal solution (in the middle of the blue line) DIFFERENT TRANSPORT RATES: WHICH IS THE BEST WAY? Each specific transport technology has a concave cost = there is a discount in the freight rate as the distance increases → There can be a concave shape which could be a road transportation like a truck and then a rail transportation which is further from the producing site o So, you look at the curve which is the lowest until intersection with next curve 22  For tiny distance: road For mid distance: rail For long distance: sea ➔ The intersection between the lines show the possible solutions MULTIMODAL TRANSPORTATION: CHOICES AND TRADE OFFS MULTIMODAL TRANSPORTATION: A REALISTIC REPRESENTATION 23 DEMAND SIDE AUGUST LÖSCH (1939) ANALYZED THE LOCATION FIRM BY THE DEMAND SIDE: by the market areas (while Weber analyzed it by the supply side) → Lösch Represents a bridge between the ‘least cost’ and the ‘demand side’ approach and it is an anticipation of the general equilibrium analysis of location Assume Scattered consumers (not concentrated in few market locations) Price elasticity of demand is negative + finite (positive in absolute value) o Weber: infinite o Hotelling: zero Costs are assumed constant → Producers will seek the location granting customers the greatest sales volume →By attempting to supply the largest possible market area, they will maximize their revenues and consequently maximize profits (if costs are constant) Spatial Equilibrium in this model determined by 2 Tendencies A) Incumbent firms want to maximize profits: these firms are already located in the market, but they want to maximize profits by enlarging their market area → Becoming monopolist B) Outsider firms want to enter the market: their entry will reduce the size of the market for the incumbent firms EQUILIBRIUM MUST SATISFY 5 CONDITIONS 1) Location of each firms must be as advantageous as possible 2) Number of firms must be enough to cover entire space 3) Areas of abnormal super-profits must disappear 4) Production/supply areas must be as small as possible 5) Borders between market areas must be iso-price for consumers: consumers should be indifferent in buying goods from industry A, or B → Prices must be equal → Lösch does not explicitly calculate the analytical solution of the location model → BUT he states that the optimal location for industrial production (and consumption) is clustered, while for agricultural production is uniformly distributed o He has shown that the spatial clustering of firms can be caused by factors other than agglomeration economies o BUT Limitation = inter-firm relationships conceived only as competition, no interaction USUAL DEMAND FUNCTION INTRODUCING SPACE = DISTANCE (therefore costs) 24  If the first was the demand function for a firm = firms producing in perfect competition: inverse relationship between price and quantity If we consider a realistic version including space: price that consumers pay for a given quantity = price* (or mill price) which is the price paid at the location of the firm o We consider that consumers move to acquire goods o If we image that we live westward or eastward to A, if tau is the cost per mile (freight rate) and if it is constant, then the further away you are form A, the higher the implicit price for A → Price paid* = mill price + tau times the distance (transport cost considered to be symmetrical INDIVIDUAL’S AND MARKET’S SPATIAL (ONE-DIMENSIONAL) DEMAND By combining this idea in which the demand depends on p (we know that p is the mill price+ tau times the distance), we can combine the two things Y-Axis = quantity sold X-Axis = distance The further away is the consumer from the firm (the more distance increases), the higher the price he will have to pay for transportation costs → The less quantity will be demanded → Usually distance is represented in a two-dimensional figure TWO-DIMENSIONAL ‘SPATIAL’ DEMAND LOSCH’S CONE Distance is bi-dimensional: The circle is describing the maximum distance at which you can sell a good, further away the firm would be unable to sell a single unit of the good because the p* times d is s too high The quantity bought at a certain distance is negatively related to the distance at which the consumer is located with respect to the firm P* is a fixed price, however, it is possible that if p* goes up (increase in the mean price) → It reduces the maximum distance at which you sell a single unit of the good o The cone is reduced because consumers will demand less goods o The new demand line (red one) will be parallel to the first one 25 SHORT-RUN In the short-run each firm will try to locate near the market area BUT there are some issues (see picture): a) Some consumers cannot buy the goods because they live in the gap area between the firms b) Consumers living between the black and the red firms may decide to buy goods from one of the two MID-RUN a) In the mid-run firms will try to get together in order to fill the gap, so as not to leave consumers unsatisfied b) A possible solution for consumers living between two firms is to draw a line: for example, the ‘upper’ living consumers will buy goods from the blue firm, whereas ‘down’ living consumers will buy the from the brown LONG-RUN In the long-run, the shape of the market area will become hexagonal: the shape that maximizes the ratio between the market area and the border lines - same principle as bee hives The agglomeration is due to competition, even if in theory it’ not an expected outcome You may obtain clustering in agglomeration of firms in a location due to just market forces → Serving as many consumers as possible o BUT, on the other hand, when at the same distance, consumers are indifferent between coming to you or to your opponent → They just flip a coin On average, you may think of all the consumers that are on the border: you get half of them, probability of 0,5 → A similar but more strategic reasoning is put forward by the model Harold Hotelling 26  HARNOLD HOTELLING (1929) He tried to explain if there is a horizontal differentiation of goods o However in order to present this model, you may refer to a geographical example o Because firms want to maximize market share, as far away as other firms, a result is clustering DEMAND SIDE: TO SHOW HOW COMPETITION CREATES AGGLOMERATION ASSUMPTIONS I. Homogeneous product II. No price competition: the product is the same III. Each agent assumes the other agent’s choices as given (= Cournot’s conjectures) UNIDIMENSIONAL REPRESENTATION OF SPACE (let’s assume two firms are located in a geography that is a line) EXAMPLE – TWO ICE CREAM VENDORS Firms are not engaged in price competition: price is fixed and equal for both firms o The price is set by the producers, They are located at the two extreme sides of a beach, Consumers are scattered along the line of the beach Each firm is assuming the other firm’s choice as given → Choice variable = location: the only things these firms can do is to change their location Imagine at the beginning they want to maximize their market share, the position on the opposite sides of the beach: there would be only one consumer in the middle o The further away you are from the vendor, the more you will pay: price of the ice cream + difficulty of walking barefoot on the beach to reach the vendor (transport cost) By definition, the vendors are splitting the market area in two: people which are located in the left side, will go to the blue vendor and viceversa on the right side to the red one If you are the blue vendor, what can you do to increase you market area at the expenses of the red firm? → Move a little bit left, in order to reduce distances for consumers However, the red vendor can do the same thing Until, this situation is reached → The outcome is basically equal to the first situation, BUT: EQUILIBRIUM → Firms cannot move their locations anymore With this model we can clearly see that we may obtain cluster or agglomeration even if there is no advantages in doing so (= no sharing of knowledge, no spill over, no labor force sharing), but just because of competition → Clustering due to: minimization of costs + maximization of market areas (Lösch, Hotelling) 27  If we use the same kind of representation used to explain LÖSCH THEORY, in which the height of the vertical segment is the mill price (= price that is charged at the location of the firm) → As you move away from the location of the firm, the price increases at a linear function with the slope of the distance → Martini Glass o The transport fee is the same, therefore the slopes are the same The situation labelled with X is one in which the firms are located right in the middle: consumers living there will pay the lowest price, since they are close to the firms, while consumers living far away will pay the price + transport costs However, with regards to social welfare, the middle point is not the best: The best location is ¼ and ¾ (point A and B) o In fact, if both vendors are located in X, the consumers located in O and L will have to walk half distance to reach the vendors → Therefore the equilibrium is not always the best possible location Who is gaining from the equilibrium? – Welfare Implications The gain of firms located in A and B is much higher than the gain if two firms are located in X Welfare implications: minimum differentiation is on average bad for consumers → Larger average transport cots, lower welfare However: centrally located consumers gain, peripherally located ones lose → In the green area: consumers enjoy lower costs o Green area = above price schedule of firms located in X, BUT below price schedules of firms located in A and B → To measure these costs you look at the size of the area of the price schedule of firms located in X and at the area of the price schedule of firms located in A and B o Here we are showing that the market equilibrium is already producing a sub optimal result so, the minim differentiation is not just bad for the peripheral consumers but also bad from a welfare point of view, because the gain of the central located consumers are more than outweighed by the losses of all other consumers → The yellow area: above firms located in A and B, BUT below firms located in X o The sum of the two yellow areas is much higher than the sum of the green area → The white area: below every area → so not interesting for us → WITH THIS HOTELING MODEL WE INTRODUCE: we may obtain clustering with competition and there is no insurance that what the market decides is by definition optimal, but it can be the best strategy for the firm, but also produce a suboptimal result from the welfare point of view 28 EX OF ICE CREAM VENDORS The ice cream vendors, locate so as to minimize the distance that the average consumer must walk Now imagine that the 2 firms are located either in ’x’ or one in 0 and the other on 1 o There would be some consumers that are located the most distant of the vendors and they have to walk half of the length of the beach Now instead imagine what if the location would be instead in A and B o In fact ¼ and ¾ of the length of the beach o Now the maximum distance that the consumer have to walk is a fourth → The best location if the location is not decided by the market, by some central planner, would be a quatre and a quatre Consumer Costs Now if we represent the cost the consumer has to bare = the mid price + the transport cost o If you compare the location that is chosen by a central planner locating the 2 vendors of ice cream in ‘x’: some consumers who are located in the middle would enjoy lower transport costs o Consumers located in the green area closer to the center are happier if the firms are located in ‘x’ BUT if the firms are located in a: consumers located between 0 and 3/8 of the distance, and consumers located between 5/8 and 1 → Have to bare lower costs than those in ‘x’ If I compare the yellow areas (of the location of the market) with the green area which is exactly the opposite → The market location is better the central planner location The market solution is not an optimum one: If we just look at the welfare, consumers will chose the market planner solution o But this is not an equilibrium, the producers will go to the (missing) The welfare solution would be the minimum differentiation → suboptimum solution. It is not just suboptimal, you also discriminate the consumers 29 LAND UTILISATION APPROACH JOHAN HEINRICH VON THÜNEN (1826) In 1875 he developed a model in which the location patterns of productive activities achieve better (= more central) locations by being able to pay higher rents o Rent: price of space/land (provides utility as a necessary and scarce production input) ASSUMPTIONS 1. Space = infinite homogeneous plain (fertility and transports) 2. There only is one central place acting as a market place (M) 3. All inputs are ubiquitous 4. Technology: fixed coefficient and constant returns to scale a. Quantity of output (x) per unit of land is fixed for each good (labor, fertilizer etc.) b. Production costs (c) are fixed 5. Goods’ prices (p) are exogenously given: from larger market and fixed o Intuition for why prices are exogenous and fixed: All producers sell their goods on a perfectly competitive market → Producers are price takers as firms in perfect competition 6. Unit transport costs () per mile is constant: total transport costs depend on volume shipped and distance , and vary from good to good o The transportation cost may also depend on the nature of the good: For example you may need a refrigerator truck for some specific goods like ice cream, etc. 7. Infinite demand for each good (complementing point 5) 8. Single landlord owns the land - multiple entrepreneurs o If there is only one landlord then each farmer has to offer an amount of money to the landlord o The landlord is building an action giving the land to the farmers who is offering the higher amount of money ITS MODEL: Profits = difference between revenues and costs [average costs (c) times quantity (x)] – BUT there are also transport costs → Profits translate into rents Rent (r) is a linear function of the distance The relation between r is a negative one: there is indeed a minus sign before tao delta x o Tao is linear and constant o The rent depends on the distance Vertical intercept: maximum amount of the rent obtained, by producing a given crop o There, the rent is just a function of the distance  which is equal to 0 → (p – c)x → The higher is the production profit, the higher would be this point (p – c)x Horizontal intercept: maximum distance at which a good can be transported o Production unit profit divided by tao (transport fee) o (p – c)/  o On the denominator there is tao (transport fee) in kilo per mile → The higher is tao (the smaller the fraction - since denominator), intercept moves left, steeper function 30 → When tao is lower, the X-intercept moves to the right and the function becomes less steep and flatter TWO-DIMENSIONS Imagine that for a specific crop you have a steep function: due to the fact that you make lots of profits selling that crop (in graph tomatoes) and on the other hand transport fees are very high (tomatoes are fragile, transport them with care which is costly) There are other producers, producing different goods (ex. corn): corn offers a profit for per square mile → p-c is lower and the vertical intersect is lower o Lower transport cost for corn (cheaper technology and no need for special containers) → tao lower than for tomatoes = horizontal intercept much higher (right) The cheapest product that you can grow is cattle What really matters for the landlord is to obtain the highest possible rent: defined by the highest function o We look at the interception of tomatoes and corn, and cattle and corn o Most convenient crop until 1st interception = tomatoes → Then corn (until 2nd interception) → Then cattle until X-intercept (where rent = 0) VON THÜNEN + FACTOR SUBSTITUTION = BID-RENT MODEL ALONSO, MILLS, MUTH, EVANS CHANGE IN ASSUMPTION: WE SWITCH FROM A FIXED TO A VARIABLE COEFFICIENT (Ass. 3)** → Instead of having straight isoquants, we use convex isoquants (including both land and non-land inputs) In The 60s Students Tried To Use His Model, to see if we could get some useful insights on how agricultural activity was geographically centered, by not only looking at the agricultural activity, but also other activities ASSUMPTIONS 1. Space: infinite homogeneous plain (fertility and transports) 2. There only is one central place acting as a market place M 3. Technology: convex isoquants** o Land and nonland inputs are imperfect substitutes o Constant returns to scale o Isoquants = locus of points that include all points corresponding to specific combinations of two inputs that give the producer a given quantity of outputs o Isocosts = how much money should I spend to produce a give amount of output 4. Goods’ prices (p) are exogenously given: from a larger market and fixed 5. Unit transport cost () per mile: constant + total transport costs depend on volume and distance () 31 6. Perfect competitive market for each industry a. Free entry b. In the long run: Zero profits across all industries (if there is a possibility for making profits, more firms will enter the market, and since firms are price takers and cannot change prices, prices will decrease following a larger supply and profits decrease too) *IN THE ORIGINAL VON THÜNEN: the producer is by definition obliged, once decided how many tons of a crop he wants to produce, to use a certain amount of input (= land) o Producer totally insensitive to the relative price of land and non-land inputs, since he was combining them in fixed proportion o Consumers: tangency point between budget line and indifference curve o Producer: you look between a tangency point between the isoquant and the isocost EXAMPLE: MACHINE TO PRODUCE BICYCLES – FIXED RATIO OF COEFFICIENTS One machine, three workers: One puts the bicycles into the machine, one governs the machine and one puts the bicycles out of the machine o Imagine adding another machine always with three workers: no sense, since no workers to use it o Imagine adding another worker with the same machine: not necessary, since not making anything → Point on horizontal line of isoquant → Extra NonLabor input, BUT no extra output **WHAT IF LAND AND NONLAND INPUTS CAN BE USED ON A VARIABLE RATE? RECAP: If isoquants are linear it means that the two inputs are perfect substitutes and what really matters is the sum of the two inputs and this also tells that you can produce a given amount of a good with using a huge amount of labor and no capital and vice versa If on the opposite case, the isoquants are L shaped, only the combination at the kink is the one possible to produce a quantity On graph: slightly bending the L-shaped isoquant IF workers very costly compared to land = Steep isocost: tangency in upper part of isoquant → Using a lot of land with a limited quantity of workers IF land is very costly compared to workers (NonLand) = Flat isocost: tangency in lower part of isoquant → Using a lot of workers and a very limited quantity of land (lower right point in picture) → Entrepreneurs try to save on costly inputs and exploit from abundantly cheap inputs 32  We are assuming that Land and Nonland inputs are described by a convex isoquant: I can use different combinations of these inputs, to produce the same quantity of output o But convex = in order to produce a unit of the good it is necessary to have a positive amount of both inputs Assuming that transport costs are linear – Transport C = *x THE BID RENT CURVE IS TELLING US THAT As we move away from M (towards right on X-Axis), land price falls: If non-land inputs stay constant, firms will substitute non- land with land input (since cheaper) As we move towards M, land price rises: If non-land inputs prices stay constant, firms will substitute land inputs with non- land inputs → A rational entrepreneur will substitute more expensive input, with less expensive input Slope of bid-rent curve: /S o Numerator = transport fee (fixed) o BUT S is variable (= land area surface used in production) In reality it is not just the distance from the city center → depending on proximity to metro, amenities, proximity to some unpleasant areas are also shaping the price of the land EX: Think about where car dealers are located in a city (like Milan): generally on the outside, since they need a large space → Also a role of agglomeration economies (often you also want to try the car) → So often car dealers tend to cluster near one another → A steeper function (= when using lower quantity of land and higher quantity of nonland) → Explains why in the city center of Milan you have a lot of banks and financial companies, whereas car dealers, malls etc. are located in the periphery The more you go to the city center, the smaller become activities (= less land used) Concentric structure of the economic structure of the city that is driven by the land market If the firms willing to use the land are significantly more than those who lend the land, it is a sort of fixed price 33  BID RENT MODEL The lower is the price of the land, the higher would be the bid-rent function on this picture → The higher would be the profitability for this firm o Since the price of the land is the cost that firms have to add up on all other costs Different industries may have different bid- rent curves and again we may assume that there are some sectors that tend to use land input illegally, therefore in the relative curves they are located further away from the city center (since tao/S, and you are using less S) CENTRAL PLACE THEORY (WALTER CHRISTALLER, 1933) A model which looks at space as a surface in which the economic activity is placed and adds the layer of hierarchic cities (= not looking at a specific market but at several markets, cities) o Not just the location of firms around a market place or just firms that try to minimize their transport cost, but the agglomeration of plurality. o They are not all differentiated but differ for their size His idea is to try to identify the forces that may lead to a certain urban hierarchy The question asked by Christaller is: Do we have a general model to explain how a large city is formed and whether near a large city we can find another large city? URBAN HIERARCHY is based on the FUNCTIONS and SERVICES available in a city → In a sense, he tries to make sense of complexity of plurality of cities Functional representation of urban hierarchy: a large city, a megalopolis, but then we have also a lower rank cities o In principle Christaller is assuming that the difference between a city and a village is that there are some functions that you cannot find in a village (ex. bakeries are in every village, Sometimes bank branches are in some villages, BUT universities are in larger cities) → Some functions can be found in higher level agglomerations and can’t be found in lower ones o Christaller is assuming that any function that is present in a lower-level agglomeration is also present in a higher agglomeration (A bakery is present in a village and also in a city) It is also related to POPULATION o Higher level agglomerations host a larger population: the people providing most services and those working are also consumers 34  o Those functions and services attract people from the urban areas as well as the hinterlands: you either try to live in a higher agglomeration, or if not, live in a lower agglomeration but closer to the higher rank agglomeration Every urban center has a given economic reach Central places compete with each other to provide goods and services o If there are a plurality of agglomerations, two cities with the same type of function, will compete against each other to provide goods and services → The idea that in order to increase the number of consumers that would be served in 2 large cities, the two cities should be very far from each other. CENTRAL PLACE THEORY: BASIC CONCEPTS Developed by Christaller in 1933 “central Places in southern Germany” It is a deductive theory to explain the number, location*, size, spacing and functions of settlements in terms of the services they perform for surrounding hinterlands → Puts together several models by Lösch o *Absolute location and relative location The theory is based on a simple extension of market-area analysis: Market areas vary from industry to industry, depending on scale economies and per capita demand → Every industry has a different location pattern → Central Place Theory shows how the location patterns of different industries are merged to form a regional system of cities The theory answers two questions about cities in a regional economy: o How many cities will develop? o Why are some cities larger than others? Central place: a service center, a settlement providing a range of goods and services to the surrounding areas Centrality: the functions or services provided by the central place o The higher the centrality of a central place is, the more the variety of services and the higher the order of services it provides o Economic reach is a measure of centrality o Centrality is crucial to the development of urban places and their services areas Hinterlands: the market areas covered by the services of central places that may include smaller central places o The higher the centrality of a central place is, the larger the hinterlands SERVICE RANGES Range: the maximum distance (r) people are willing to travel (to a center) to use a service – Depending on different kinds of services o EX: For a bakery the range is quite small, whereas for university it is bigger o The maximum area to be served from center = circular region of radius r Threshold: the minimum radius (r’) identifying the area corresponding to the minimum number of people required to support the service o Circular service area with sufficient population to generate sufficient demands, to make the central place economically viable to provide the service 35 CHRISTALLER attempted to design a model that would show how and where central places in the urban hierarchy would be functionally and spatially distributed 5 ASSUMPTIONS 1. The surface of the ideal region would be flat and have no physical barriers (homogenous plain) 2. Soil fertility would be equal everywhere 3. Population, therefore purchasing power is evenly distributed o People needed to produce on the land, so they were heavily distributed and there was less concentration 4. Uniform transport network permit direct travel from each settlement to the other 5. Constant maximum distance, or range, for the sale of any good or service produced in a town prevails in all directions from the town center To calculate the degree of centrality Central goods and services are those provided only at a central place Range of sale = the distance people would be willing to travel to acquire goods and services Model evolved consisting of perfectly fitted hexagonal regions Hierarchy of Settlements Hamlet: fewest goods and services available o Lowest possible settlement Village: includes the region of the hamlet and some additional goods and services Town: includes the region of the village and hamlet and provides some additional goods and services City: includes the region of the village, hamlet and town and provides additional goods and service A CENTRAL PLACE is a settlement which provides one or more services for the population living around it o Simple basic services (e.g. newsagent, grocery stores) are said to be of low order while o Specialized services (e.g. consultancy firms, universities) are said to be of high order → The level of service that a place is providing is determining the rank of that settlement Low order settlements = settlements which provide low orders services High order settlements = settlements which provide high order services → Having a high order service implies there are also low order services around it, but not vice versa CONCLUSIONS The larger the settlements, the fewer their number → Connection with Zipf’s law The larger a settlement, the farther away a similar size settlement is The Range increases as the population increases. The larger the settlement, the higher the order of its services DISTINCT RULES The larger the settlement → The less there are of them → The farther apart they are The less there are of a settlement, the larger the hinterland, or sphere of influence, of its goods and services Places of the same size will be spaced the same distance apart 36  WHY HEXAGONAL SHAPES → With hexagonal shapes, there are neither overlapping, nor voids Christaller assumed that in the absence of geographical obstacle, an urban hierarchy will always appear. Exagonal shapes are intertwined with hamlets. THREE PRINCIPLES OF CENTRAL PLACE THEORY 1. MARKET PRINCIPLE a. There should be a minimum number of central places of all sizes so that trade profits could be maximized; b. The whole population of the area should be supplied with all goods and services → Note how the two cities (red) are far away 1 + (6 corners/3 Hexagons) → 1 + (6/3) = 3 → K = 3 ** Maximize distances between lower order centers in order to maximize their market areas Deriving the highest-order central places The upper limit of the highest order services is used to obtain the area for central places ranked ‘B’ to meet marketing principle Each central place serves a circular area (in the short run) with radius r and the distance between any two central places is 2r o But some areas are not served To meet the marketing principle, a modified structure of overlapping circles is thus necessary Assume consumers go to the nearest central place: exclusive regular hexagonal areas are obtained as the service areas for B centers Deriving the lower-order central places For goods and services with a smaller upper limit of range, some areas cannot be served by B centers, thus lower-order K centers are needed o To be most competitive with B centers of higher levels, these K centers will be located in the center of areas not served with maximum distance to all B centers **These K centers are also the 6 corners of the hexagonal areas of B centers o K=3 framework: Each B center service area will cover three K center service areas, this is called 37 2. TRANSPORTATION PRINCIPLE Low-order centers will develop along the transportation lines, linking the higher-order corners K centers will locate in the middle of the transportation link between any two B centers K=4 framework (traffic principle): Each B service area covers four service areas of K centers 1 + (6 sides/2 Hexagons) → 1 + (6/2) = 4 → K = 4 3. ADMINISTRATION PRINCIPLE The service areas of k centers should be COMPLETELY contained by a service area of B center K = 7 framework (or separation principle): Each B center service area covers 7 service areas of K centers o The market area of each center, contains six complete centers of the next lower order 1 + (6 small hexagons) = 7 → K = 7 CONTRIBUTIONS OF CENTRAL PLACE THEORY It defines the ideal pattern of central places and explains why an urban hierarchy may exist in the reality o A small number of high-order centers serve a large hinterland o Many low-order centers serve a small hinterland It provides a basis for planning size, number and spacing of centers in newly-developing areas → Christaller’s model is bringing back some results we show as a empirical regularity which come from the Zipf’s law LIMITATIONS 1. A uniform transportation surface, the relative accessibility between ay two points, is a direct function of their distance 38  o In reality, there are transport routes and natural barriers like rivers and costal lines o Accessibility is different over space o When we look at the historical realization, we should take into consideration the geographical position 2. Population is evenly distributed o In reality, population density is uneven → Often clustered o Other than s