This project (a work-in-progress) is about analyzing Crunchbase data in order to learn about gender equity in start-ups and VC funding.
There is one main dataset from which all others are derived that relates start-ups, gender, and funding data: where 1 row = 1 company. I only pulled those founded after 1989 to visualize a more relevant period. On any given row, there is information on the industry sector, the genders of the founders, and the funding history of any given company. Note: only U.S. companies were queried, but the founders of those orgs are not necessarily from the U.S.
The remaining datasets bin the data in the following ways: by investor, by education level of the founder, by industry (to find where the disparities are), by U.S. state, and by total funding for every year between 1990-2021.
"Diverse" means founders, who were identified as female, non-binary, or unspecified.
Data for the above graph from year_funded.csv & code from bin_year_funded.py
Data for the above graph from year_funded.csv & code from bin_year_funded.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph: year_funded.csv & code from rates.py
Data for the above graph from industries_count.csv & code from bin_industries.py
Data for the above graph from industries_count.csv & code from bin_industries.py
Data for the above graph: org_funding.csv & code from post_process.py
Data for the above graph: industries_count.csv & code from bin_industries.py
Data for the above graph: industries_fraction.csv & code from bin_industries.py
Data for the above graph: state_count.csv & code from bin_states.py
Data for the above graph: state_fraction.csv & code from bin_states.py
Data for the above graph: degrees_fraction.csv & code from process_degrees.py
To produce the organizations.csv:
- crunch_library.py
- organizations.py
- people.py
organizations.csv: where 1 row = 1 company with gender info
To produce the org_funding.csv:
- crunch_library.py
- organizations.py
- people.py
- funding_rounds.py
- post_process.py
org_funding.csv: where 1 row = 1 company with gender info and the money raised at each funding round (see last column)
More info: I used 3 queries to get this data from 3 relevant collections: organizations, funding rounds, and people. I pulled from the people collection for the gender of each founder and associated them back to their respective organization. I got the history of each company's funding from the funding rounds collection and added the history as a list within a column called moneyRaised.
To produce the binned data of total funding per each year since 1990:
- bin_year_funded.py
year_funded.csv: binned by year starting from 1990, where 1 row = aggregate data over 1 year
To produce the binned data by industry:
- bin_industries.py
industries_fraction.csv: contains binned data for fraction of gender within each industry where 1 row = aggregate data for all companies for a given industry
industries_count.csv: contains binned data for total count of genders of founders within each industry where 1 row = aggregate data for all companies for a given industry
To produce the binned data by investor:
- bin_investors.py
investors_fraction.csv: contains binned data by investor where 1 row = aggregate data for all companies for a given investor
To produce the binned data by U.S. state:
- bin_states.py
state_fraction.csv: contains binned data of the fraction of founders by gender within each state where 1 row = aggregate data for all companies for a given state
state_count.csv: contains binned data of the total founders by gender within each state where 1 row = aggregate data for all companies for a given state
To produce the binned data for founder's education level by money invested:
- process_degrees.py
degrees_fraction.csv: contains binned data of the fraction of money invested towards founders by gender where 1 row = aggregate data for different education levels for a given gender
degrees_count.csv: contains binned data of the total money invested towards founders by gender where 1 row = aggregate data for different education levels for a given gender