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Focusing on the FDI patterns of location, the goal of the analysis is to describe and characterise the spatial distribution of FDI investments into London, and more specifically, to investigate differences in the spatial concentration, or clustering, between investors according to their sector, function, and originating country. Spatial clustering of certain communities, activities, or functions would imply that the firms draw some co-location benefits from this

agglomeration.

As part of its mission, UK Trade and Investment²³ (UKTI) records and collects information on all foreign investments into the UK. For the period of 2006-2007, UKTI provided records of investments for the London region to this research project. This dataset records investments, including mergers and acquisitions, along with the number of jobs created or safeguarded, as well as investors’ details such as geographic provenance, industry sector, and function.

Company records are geocoded to the postcode level for 735 firms in the London area and wider South-East. An overview map of the spatial distribution of the total dataset can be seen in Figure 12.

23 UK Trade and Investment (UKTI) is a UK Government organisation, which supports UK-based businesses in international markets and promotes and supports inbound FDI to the UK.

Figure 12: FDI business locations in Greater London.

The distribution of FDI across London for the financial years of 2006 and 2007 can be mapped in a similar fashion to the general levels of economic activities presented before. As the dataset does not have any reliable counts of the size (e.g. number of jobs) for each investment, the analysis only investigates the density of co-location of points, disregarding the size of individual firms. Kernel Density Estimation (KDE) allows the estimation of the density of point patterns, allowing for a visualisation of patterns of concentration of activities in Figure 13. The bandwidth for the KDE estimation (in this case 1 kilometre) is chosen to represent the spatial scale of town centres across London. A good example of this can be seen in the zoomed-in view of Central London (see Figure 14), representing the point pattern along with the resulting KDE surface.

Figure 13: Jobs concentration of Greater London

Figure 14: FDI location concentration in Central London

Comparing between the map of general economic activity levels and the map of FDI

investment concentrations offers no significant differences from the visual inspection of these two surfaces. Most FDI investments still focus on Central London business areas such as the City and the West End, with secondary centres of investment in Canary Wharf and West London, including Hammersmith, which can be attributed to general urban agglomeration economies. The question then is, if the spatial structure of FDI investments presents patterns of concentration above and beyond purely urban agglomeration economies, attributable to industry sector, function or provenance clustering effects? The following section presents a review of relevant spatial measures of such effects, and the methodology used in the work for the qualification of the clustering of FDI investments.

3.2.2.1 Methodology

Economists have traditionally used concentration indices to determine whether there is agglomeration or dispersion of firms in a given territory (Marcon & Puech 2003), such as the Gini index and the G index, proposed by Ellison & Glaesner (1997), which also incorporates the size of firms. Marcon & Puech (2003; 2006; 2007) note that for these measures, any evidence for spatial clustering is only valid for a specific spatial scale, and indeed can be an effect of the Modifiable Aeral Unit Problem²⁴ (MAUP). Nearest neighbour distance-based measures such as Ripley’s K and the derived Besag’s L function look at inter-point distances without relying on the aggregation to areal units, thus circumventing the MAUP that plagues the Gini or G indices.

Marcon and Puech recognised the limitations of Ripley’s K and Besag’s L function²⁵ and developed their own function M. According to Marcon & Puech (2007), the M function allows for the comparison of an economic sector to the aggregate activity (represented by all

sectors), as a cumulative function counting neighbours of points up to a given distance r.

24 Modifiable Areal Unit Problem (MAUP): A problem arising from the imposition of artificial units of spatial reporting on continuous geographic phenomena resulting in the generation of artificial spatial patterns.

25 Distance-based measures such as Ripley‟s K and Besag‟s L function analyse concentration or dispersion by counting each firm‟s average number of neighbours within a circle of a given radius. The actual number of firms is then compared against the expected equivalent according to a spatial

randomness process. Ripley‟s K thus represents a measure of excess localisation or dispersion, which can be attributed to, for example, economies of scale, sector internal co-localisation economies, and general urbanisation economies. The assumption, and drawback, lying below the Ripley‟s K and Besag‟s L functions is the hypothesis of a constant density (i.e., a homogeneous distribution) of economic activity.

A more realistic assumption is that the underlying distribution is heterogeneous, for example, lakes and mountains where firms cannot locate. The same can be said of spatial patterns resulting from

urbanisation economies (e.g., the benefits of concentration of activities in Central London versus surrounding areas and suburban neighbourhoods.)

The M function takes as the starting point Plants (economic activities) which are located as points on a map. A reference point type (e.g. Sector, origin) is selected and a target neighbour type called T is defined: other companies either from the same type (intra-industrial) or of a different type (inter-industrial). The average number of target neighbours is compared to a benchmark to detect whether they are more or less frequent than if plants were distributed randomly and independently from each other. To control for variations of local density of points, each number of target neighbours (Ti around a point i) is normalized by the number of all neighbours in the same area (Ni). For each reference point, a ratio of target neighbours is generated (Ti/Ni ) within the distance r from each point i. The average of this ratio is

computed to the global ratio calculated from the entire territory. The M function is normally expressed as a ratio for convenience as the benchmark is equal to one:

The M function as a distance-based method controls for local variations of plant/office/firm density by normalising for each target neighbour against the total number of neighbours in the same area. The M function is normally computed for a set of distances (rmin to rmax) and presented as a continuous function on a graph, including confidence intervals for the null hypothesis of independence of plant locations.

The M function allows the investigator to analyse, on a global scale, any evidence of excess spatial concentration. The measure does not presume the presence of an underlying

homogeneous spatial distribution to investigate the spatial distribution, a big advantage over previous methods, such as the K function, and appropriate to investigate the inhomogeneous spatial distribution of economic activities across London. Secondly, the M function can be run iteratively for a set of distances, and thus can map patterns of concentration or dispersion across multiple spatial scales, negating any MAUP.

In the case of an investigation of FDI into London, the analysis first determines if there is any clustering behaviour of FDI-type businesses, above and beyond the heterogeneous structure of the underlying general economic activity patterns presented previously. Secondly, the analysis can ascertain at what spatial scale clustering of FDI activities occurs, from the street or block level to the neighbourhood and subregional scale.

3.2.2.2 Analysis

The benchmark against which the M function compares the different FDI investor’s classes was the general spatial distribution of FDI investment (753 records). The computation was done for three variables - Country of Origin, Sector, and Function - according to the classifications contained within the UKTI dataset. The analysis of the M function results highlights particular tendencies for either agglomeration or clustering of activities over and above what would be expected from general FDI investors, or inversely dispersion of activities beyond the expected average.

The M function algorithm²⁶ generates confidence levels obtained through Monte Carlo

Simulations. For the purposes of this study, the M function was generated up to 15 kilometres distance, in 500-metre steps. This is believed to represent a compromise between

computation time and a relevant spatial resolution of the curves to highlight patterns of excess agglomeration from the local neighbourhood up to the spatial scale of London regions.

Confidence levels of five percent were generated by the software to identify any significant departures from Complete Spatial Randomness (CSR) of the process.

It is important to note that the M function only provides a descriptive analysis of intra- and inter-industrial geographies, and can thus not provide any mechanistic explanations for the patterns of agglomeration or dispersion that the M function observes.