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|Is billed to=AccMcNair01|Has notes=|Is dependent on=|Depends upon it=|Has project status=ActiveTabled
Women in Entrepreneurship (Issue Brief) (view source)
Revision as of 12:40, 21 September 2020
, 12:40, 21 September 2020no edit summary
{{Project|Has project output=Content|Has sponsor=McNair ProjectsCenter
|Has title=Women in Entrepreneurship (Issue Brief)
|Has owner=Carlin Cherry
|Has start date=Spring 2016
|Has keywords=Women, Entrepreneurship
}}
=Timeline and plan for summer=
*note: I am using "hopethisworks8.txt" to make my graphs
*4-5 artifacts, 4-5 specifications, last artifact a regression table (tables, charts)
**something about data itself. pie chart (how many companies do we have, how many do we have ceo information for, how many do we have founder) and then relatedly many can we classify as men or women
***this will influence all remaining graphs - use this info to guide the rest of your graphs
***classified drs using their first name matched to common first name list, that's why we were able to classify some drs as women, some as men, and some drs but gender unknown
**graph with time on x axis, % on y axis, women founders, women ceos, women management positions (management position being anything that is vp and above) over time
***will write up these results-ie most of the women in "womens management positions"
**conditional on having (ceo identified) how many women, how many men, drs, dr men, dr women, dr unknown.
**bar graph with women in various industries, y axis will have percentage
***should also make note of total percentage of VC dollars in each industry
**regression table
***variable names, with n and r^2, industry/year fixed effects Y/N
***each one of these will correspond to a regression
***variables: IPO, acquisition, exit all correspond to 0,1
***rounds, dollars invested
=Spring 2017=
