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You can plot this data onto a map using the script located:
E:\McNair\Projects\Accelerators\EnclosingCircle\Draw_circles_test\draw_vc_circles.py
===Summary===
Clusters of economic activity plays a significant role in the firms performance and growth. An important driver of growth is the knowledge spillover between firms. This includes among others the facilitation of information flow and ideas between firms which could be a milestone especially in the growth of startup firms or small businesses. This project focuses on the effects of agglomeration on the performance and growth of venture capital backed up firms. It introduces a novel measure of agglomeration which can be used to empirically test the effects of clustering. This measure the is smallest total circle area that covers all of the startups in the sample such that there are at least n firms in each circle. The projects is based on the creation of an algorithm which gives an unbiased measure to be used in the empirical analysis. The regression we are interested in takes the following form:
[[File:regression_equation.png]]
The dependent variable is a measure of growth of the firms. This measure could be investment forwarded one period or growth in investment. The control variables include the number of the startups firms, m, the agglomeration measure, A and a vector of other control variables affecting the growth of firms at time t. Because of the endogeneity in the circle area or the measure of agglomeration, A, there is a need for an instrumental variable to get consistent estimates of the effects we are interested in. The proposed instrument is the presence of a river, or road in between the points representing geographical locations of the venture capital backed up firms. The instrument affects the total circle area without having a direct impact on the growth. This makes it good candidate for a valid instrument.
The next tasks are determining the additional control variables to include in the regression, years to include in the analysis and methods of finding an unbiased measure of agglomeration.
