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Geographic concentration of industries / localization and distance-based methods. Rosa Sanchis-Guarner GY460 17th November 2008. This seminar. In this seminar we study measures of manufacturing concentration based on 2 papers:

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## Geographic concentration of industries / localization and distance-based methods

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**Geographic concentration of industries / localization and**distance-based methods Rosa Sanchis-Guarner GY460 17th November 2008**This seminar**• In this seminar we study measures of manufacturing concentration based on 2 papers: • “Evaluating the geographic concentration if industries using distance-based methods”, Eric Marcon and Florence Puech, JEG, 2003. • “Testing for localization using micro-geographic data”, Gilles Duranton and Henry Overman, REG, 2005.**1st paper**“Evaluating the geographic concentration if industries using distance-based methods” Eric Marcon and Florence Puech, JEG, 2003**Marcon and Puech, JEG, 2003 (1)**• Economic activity is not homogeneously distributed in the space: geographic agglomeration (numerous evidence) • Traditionally concentration indices are used: • Herfindahl, Gini or Ellison-Glaeser • The problem of these indices is that they evaluate the concentration at a single geographical level • an arbitrary geographical level of clusters is chosen we compute the indices for the different levels**Marcon and Puech, JEG, 2003 (2)**• This is limited: the authors use distance-based methods to assess the concentration or dispersion of firms: • They can describe spatial distribution at different geographical levels simultaneously • They overcome the circumvent scale problem and the MAUP problem • At different scales we can find different results**Marcon and Puech, JEG, 2003 (3)**Intuition: • For each firm we count the number of its neighbours (other firms) within a given distance • We calculate the average number of neighbours for every firm at each distance • The benchmark is complete spatial randomness (CSR): firms locate independently and with the same probability (density) everywhere • If location is not random and firms locate close to other firms because it is attractive, we can expect to find more neighbours than if the location is random • The contrary for dispersion (less neighbours)**Marcon and Puech, JEG, 2003 (4)**• To determine if the distribution of firms is different from the CRS we use a mathematical function called L (a modification of Ripley’s K function). • We can have 4 types of distribution: • Homogeneous: constant density • Completely random or independent • Concentration or agglomeration • Dispersion**Marcon and Puech, JEG, 2003 (5)**• Ripley’s K function describes the spatial distribution of a set of points • λ is the average density of points (constant) • K(r) is the average number of neighbours divided by λ • For a random distribution, the expected number of points in a circle of radius r is λπr2, so for CRS K(r)=πr2 (benchmark) • The L(r) function is a normalisation of the K function in order to obtain a benchmark of zero • L(r) > 0 the distribution is geographically concentrated • L(r) = 0 the distribution is geographically independent (L is flat) • L(r) < 0 the distribution is geographically dispersed**Marcon and Puech, JEG, 2003 (6)**Procedure: • They pick an area of study (rectangular area) • They calculate K and L for a wide range of radius: count the firm’s average number of neighbours within a circle of given radius • The correct for the “edge effect” (factor of the circle's area divided by the intersection area) • They compare the actual distribution of firms differs significantly to the null hypothesis of random distribution • They generate confidence intervals by generating by simulation (Monte Carlo) a big number of independent random distributions with the same number of points and density than the sample of firms**The edge problem illustration**radius radius**Marcon and Puech, JEG, 2003 (7)**• They use French data on firms (geographic database) on an industrial area around Paris and 14 sectors • Lmin and Lmax limit the interval at which the null hypothesis of random distribution is valid • The associated L curve show significant concentration for all distances to 0 to 25 kms (for all sectors and for total manuf)**Marcon and Puech, JEG, 2003 (8)**• Compared to homogeneous distribution: all sectors and all manufacturing are concentrated at different scales. • Then they assess if some sector (particular set of firms) is more concentrated than the industry. • The benchmark is now heterogeneous: we compare the concentration of a sector relative to the average industrial density**Marcon and Puech, JEG, 2003 (9)**• In this paper the advantage is that they measure concentration by analysing simultaneously the spatial distribution of firms at different geographical scales • Preserve the continuous setting of the data • Their measure complements the existing measures (indices) • It does not take into account the individual characteristics of firms • Is very computer intensive**2nd paper**“Testing for localization using micro-geographic data” Gilles Duranton and Henry Overman, REG, 2005**Duranton and Overman, RES, 2005 (1)**• They use distance-based methods to study the location patterns of industries and particularly their tendency to cluster relative to overall manufacturing • Their approach assess the departure from randomness (no location) • The apply it to the UK manufacturing • Their findings (for 4-digit industries): • 52% are localized at a 5% confidence level • Localization takes place mostly at small scales below 50 kms • The degree of localization is very skewed • Industries follow sectoral patterns: smaller and bigger establishments follow different patterns in different industries**Duranton and Overman, RES, 2005 (2)**• Localization: tendency of an industry to agglomerate/concentrate over and above the overall economic activity • At which scale this localization occurs? • Normally indices are used to measure localization at different geographical scales • Are small or big establishments the ones which drive concentration?**Duranton and Overman, RES, 2005 (3)**Desirable requirements of a localization measure: • Comparable across industries • Control for the general tendency of manufacturing to agglomerate (in some places there is more employment than in others) • Control for industrial concentration (there are factors that make plants cluster in their location) • Gini indices satisfy 1 + 2 • Ellison and Glaeser satisfies 1 + 2 + 3 • Problem with these indices is that they transform dots on a map into units in boxes (waste of information)**Duranton and Overman, RES, 2005 (4)**Desirable requirements of a localization measure: • Unbiased with respect to scale and aggregation level (so we do not aggregate the points and we overcome the MAUP problem) • Gives and indicator of statistical significance • Their measure satisfies 1 + 2 + 3 + 4 + 5 • They will use spatial point patterns techniques: • They calculate the distribution of distances between pairs of establishments in an industry • They compare this distribution to with that of a hypothetical industry with the same number of establishments which are randomly distributed conditional of the distribution of aggregate manufacturing**Data:**1996 Annual Respondent Database ARD Information about all the UK establishments They have the postcode of the location (block) They geo-reference the data After solving some problems they have 176,106 locations Each dot represents a production establishment Duranton and Overman, RES, 2005 (5)**Duranton and Overman, RES, 2005 (6)**Methodology: • They select the relevant establishments • They compute the density of bilateral distances between all pair of establishments in an industry • They compute the counterfactuals • Same number of establishments • Randomly allocated across the existing sites • They construct the local confidence intervals and the global confidence bands at 5% level: we can compare the actual distribution to the counterfactuals to assess the significance of departures from randomness**Duranton and Overman, RES, 2005 (7)**• Step 1: consider a size threshold and only retain establishments with employment above that threshold • Step 2: Kernel estimations of K-densities • For any industry A they calculate the Euclidean distance between any pair of establishments (actual location) • To control for the noise they kernel-smooth when estimating the distribution of bilateral distances • The grid points in the x-axis are 0-180 kms distances • They control for dependence between the bilateral distances => they need to use Monte Carlo simulations to test for departures from randomness**Duranton and Overman, RES, 2005 (8)**• Step 3: construct relevant counterfactuals: • The number of firms in each industry and the size distribution is taken as given • To control for the overall tendency of manufacturing to agglomerate they consider the set of existing sites currently use by a manufacturing establishment as the set of all possible locations for any plant • They construct the counterfactuals by first drawing locations from the overall population of sites and then calculating the bilateral distances. • They run 1000 simulations for each industry and calculate the smoothed density for each simulation**Duranton and Overman, RES, 2005 (9)**• Step 4: construct the confidence intervals • They draw a sample from the distribution of all manufacturing sites and re-compute the density (step 3) • They restrict the distance grid points to 0-180 kms. • Local: they repeat step 3 1000 times for each distance grid point . The upper and lower bounds to 95% of this density estimates at that distance grid point gives the local confidence interval. The shape of these intervals reflects the distribution of overall manufacturing • Global: they find out which local upper and lower bounds would include 95% of the estimates at every grid point and this are the global confidence intervals. The statement is valid for the overall location pattern of the industry.**Duranton and Overman, RES, 2005 (10)**Interpretation: • Local (dotted lines): localization (dispersion) is detected when the K-density of one particular industry lies above (below) its local upper (lower) confidence interval • In the graph: • industry D exhibits localization for every km in [0-60] • Industry C exhibit dispersion in the same range • Global (dashed lines): global localization is detected when the K-density of one particular industry lies above its upper confidence interval. Global dispersion when the K-density lies below the lower confidence band and never lies above the upper confidence band • In the graph: • industry D exhibits global localization • Industry C exhibits global dispersion • Industry B exhibits neither global localization nor dispersion • Industry A exhibits global localization and no dispersion**Duranton and Overman, RES, 2005 (12)**• Basic results for the UK firms • 52% of industries are localized, 24% are dispersed and 24% do not deviate from randomness • Localization is not as widespread as found in other studies • Localization takes place at fairly small scales • Deviations from randomness are very skewed across industries • Industries that belong to the same branch tend to have similar localization patterns • Other things they do • Study the effect of the size of the establishment • After censoring for small only 43% show localization • Weight for employment • Compare with EG index • Do it for 3-digits and 5-disgits classification**Duranton and Overman, RES, 2005 (13)**Main findings: • 51% of four-digit industries exhibit localisation at a 5% confidence level and 26% of them show dispersion at the same confidence level. • Localisation in four-digit industries takes place mostly between 0 and 50 kilometres. • The extent of localisation and dispersion are very skewed across industries. • Four- and five-digit industries follow broad sector and branch patterns with respect to localisation. • In some industrial branches, localisation at the industry level is driven by larger establishments, whereas in others it is smaller establishments which have a tendency to cluster. • • Localisation and dispersion are as frequent in three-digit sectors as in four-digit industries for distances below 80 kilometres. Three-digit sectors also show a lot of localisation at the regional scale (80 − 140 kilometres) due, at least in part, to the tendency of four-digit industries to co-localise at this spatial scale**Compare both papers**• Marcon and Puech compute the number of establishments within a given radios • Duranton and Overman find K as the average number of points located at a distance r from each firm

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