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*This page [[Hubs (Academic Paper)]] contains only the abstract and some useful refs
*The main [[Hubs]] page is the place to go!
*There is also [[Old Completed Work on Hubs]]
*For a high-level overview of the variables for the scorecard go to [[Hubs Scorecard (Academic Paper)]]. This summarizes:
**Current work in progress for building the Hubs scorecard: [[Hubs: Hubs Scorecard]]
**Tracking of work in progress for the scorecard [[Hubs: Hubs Data Building]]
Entrepreneurship hubs have recently emerged as a stable institutional form and as popular and important components of entrepreneurship ecosystems. Hubs are membership-based co-working flex-spaces with specialized services and resources for nascent start-up firms. Examples of hubs include the Capital Factory in Austin, Texas, 1871 in Chicago, Illinois, and 1776 in Philadelphia, Pennsylvania. Each of these hubs has around 50,000sqft of workspace for almost a thousand members working at hundreds of start-ups. Each also includes an accelerator program, has daily events, classes and meetings related to entrepreneurship, and hosts venture capitalists, angel investors, and service firms.
Hubs provide a very high degree of agglomeration. Agglomeration is particularly important in entrepreneurship because it facilitates learning and failure is frequent. Entrepreneurs can then learn from other entrepreneurs as well as industry professionals; and when a start-up based in a hub fails, the firm’s human resources can be quickly and efficiently absorbed into another venture. We might therefore expect that the introduction of a hub will lead to a greater degree of entrepreneurial activity in a region.
This paper will use a difference-in-difference approach to estimate the effect of the introduction of a hub on seed and early stage venture capital investment in an area. The empirical methodology of the paper is closely aligned with the methodology in Fedher and Hochberg (2015). The decision of a hub to locate itself in an area is expected to be highly correlated with existing characteristics of the area, unobserved in the data, which induces a significant endogeneity bias in the model. To rectify this issue the methodology proceeds in two steps. In the first step, a hazard model is estimated which predicts the probability that a hub will come to an area. In the second stage these predicted probabilities are used to find a match for each treated region by finding the untreated region with the most similar probability of founding an accelerator in that year when the treated region is on the common support.
Currently there are '''3''' major tasks being performed (list to be updated):
#'''Creation of VC data table''': '''UPDATE: Complete''' (see completed work section below)
#'''Creation of Hubs Dataset''': '''UPDATE: See current work in progress for updates''' We will collect key variables for potential Hubs.
#'''Hazard Rate Model''': '''UPDATE: (7/11) Spoke to Xun Tang, econometrics professor in Rice's Economics Department, and now looking for appropriate proportional rate hazard models with time varying covariates.''' In order to perform our diff-diff model, we need to match MSAs. In order to do so, we will be using a hazard rate model to produce a probability that a MSA gets a Hub and compare MSAs that do and don't have hubs with similar probabilities.
* A general overview of entrepreneurial ecosystems can be found here: [[Entrepreneurial Ecosystem]].
* Yael Hochberg and Fehder (2015), located in dropbox
** Use this paper as a guideline on how to conduct the analysis
*US Census Bureau data on employment by MSA: http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_14_5YR_B23027&prodType=table
*USPTO utility patents by MSA: http://www.uspto.gov/web/offices/ac/ido/oeip/taf/cls_cbsa/allcbsa_gd.htm
*MSA level trends: http://www.metrotrends.org/data.cf
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