The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment

McNair Project

The Impact of Entrepreneurship Hubs on Urban Venture Capital Investment
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Abstract

The Hubs Research Project is a full-length academic paper analyzing the effectiveness of "hubs", a component of the entrepreneurship ecosystem, in the advancement and growth of entrepreneurial success in a metropolitan area.

This research will primarily be focused on large and mid-sized Metropolitan Statistical Areas (MSAs), as that is where the greater majority of Venture Capital funding is located.

Data

Venture Capital Transactions Data Set

The Hubs data set, from SDC Platinum, has been constructed in the server:

Data files are in 128.42.44.181/bulk/Hubs
All files are in 128.42.44.182/bulk/Projects/Hubs
psql Hubs

The data set includes all United States Venture Capital transactions (moneytree) from the twenty-five year period of 1990 through 2015. Data has been aggregated at the portfolio company, fund, and round level. It will be analyzed at the combined MSA level. We will be looking at in terms of number of companies funded in number of funds active, and flow of investment in a given MSA.

Raw data tables

1. Funds: fund name, first investment date, last investment date, fund closing date, address, known investment, average investment, number of companies invested, MSA, MSA code.
2. Rounds: round date, company name, state, round number, stage 1, stage 2, stage 3
3. Combined Rounds: company name, round date, disclosed amount, investor
4. Companies: company name, first investment, last investment, MSA, MSA code, address, state, date founded, known funding, industry
5. MSA list: MSA, MSA code, CMSA, CMSA code
6. Industry list: changes 6 industry categories to 4— ICT, Life Sciences, Semiconductors, Other

Grandeur Table (Fund-Round-Company)

The final table contains all venture capital transactions by disclosed funds and portfolio companies, together with their CMSAs. To get the table, we processed the raw data sets in the following steps:

1. Clean company data
   1. Import raw data companies
   2. Add variable 'CMSA' from data set MSA list, update variable 'industry' by joining data set industry list
   3. Remove duplicates and remove undisclosed companies
2. Clean fund data
   1. Import raw data funds
   2. Add variable 'CMSA'
   3. Remove duplicates and remove undisclosed funds
   4. Match fund names with itself using [The Matcher (Tool) |The Matcher] to get the standard fund names
3. Clean round data
   1. Import raw data rounds and combined rounds
   2. Add variables 'number of investment', 'estimated investment' and 'year'
   3. Remove duplicates and remove undisclosed funds
4. Combine companies and rounds
   1. Combine cleaned companies and rounds data table on company names
   2. Add variable 'round number' and 'stage'
   3. Remove duplicates
5. Combining funds and rounds-companies
   1. Match fund names in rounds data table with standard fund names using [The Matcher (Tool) |The Matcher] to standardize fund names in rounds data table
   2. Join standard fund names to rounds-companies table
   3. Join cleaned funds table to rounds-companies table on standard fund names

Supplementary Data Sets

Supplementary data sets are cleaned and joined back to CMSAyear table on CMSA and year.

• Number of STEM graduate student, by university and year(2005 to 2014).

 E:\McNair\Projects\Hubs\STEM grads for upload v2.xls

• University R&D spending, by university and year(2004 to 2014).

 E:\McNair\Projects\Hubs\NSF spending for upload.xls

• Income per capital, by MSA and year(2000 to 2012)

 E:\McNair\Projects\Hubs\Income per capita upload.xls

• Wages and salaries, by MSA and year(2000 to 2012)

 E:\McNair\Projects\Hubs\Wage for upload v2.xls