The document summarizes Phoebe Wong's background research project on characterizing the employment trajectories of computer science graduates from elite universities. It describes the People.co and Compustat datasets being used, which contain profiles of over 7,000 CS graduates and financial data on publicly listed companies. It outlines the data cleaning process, including standardizing values, removing stopwords, using MTurk to identify company synonyms, and using Alchemy API for entity recognition. Preliminary results show the impact of recoding original company names based on the cleaning steps. The future goal is to merge Compustat data into the People.co data.

1. Careers, Professions, Entrepreneurs and R
The Berkeley R Language Beginner Study Group
Phoebe Wong
With Weiyi Ng and Professor Toby Stuart
Haas School of Business, UC Berkeley

2. My Background
• Senior Psychology student at Cal
• Research experience
1. Lab manager of experimental social psychology
2. Research assistant at computational sociology
• Programming experience
• Learned R and Python a year ago because of interest, love it!
• Used R in honor thesis

3. Background of my project
• Careers, entrepreneurship
• What are we interested in?
• Characterizing employment trajectories of CS graduates from the 9 elite CS
programs
• MIT, Stanford, Berkeley, Harvard, Princeton, UIUC, CMU, UTXA, UMCP
• involves classifying employers by employment characteristics (e.g. industry,
size, age)
• Why CS?
• Base rate = 10% for CS (random sample base rate = < .05%) for high potential
entrepreneurship
• High-tech firm incorporation is 69% higher in 2010 since 1980 (while general
private firm incorporation decreases)

4. People.co (my.emp.df)
• Profiles of CS graduates from the 9 elite CS programs
• MIT, Stanford, Berkeley, Harvard, Princeton, UIUC, CMU, UTXA, UMCP
• From 1995-2005
• All 3 degree levels included (BA, Masters & PhD)
• N=7160 profiles, with 6064 complete profiles

5. • What’s in the dataset?
• N=6064 profiles, multiple rows per person
People.co (my.emp.df)

6. Compustat (empmeta.df)
• Database of financial, statistical and market info on active & inactive
global publicly-listed companies
• 1950-2015
• What’s in the dataset?

7. Compustat (empmeta.df)
• Example: Bank of America

8. Top 40 employers of CS graduates(my.emp.df)

10. How much does the top 40 companies tell us?
4000

11. Clean up (1): Basic clean up
Lower case
Remove non-numeric or alphabets
Standardizing white space
Character encoding
Remove space at the beginning & at the
end that happens for 1 or more times

12. Clean up (2): Stopwords
Assigning company
names from Compustat
data to a variable
cleanup()
Tokenize

13. Clean up (2): Stopwords (cont.)

14. Clean up (3): Mturk
• ‘grep’ names of top 200 companies from Angelist
• Ask mturkers to identify synonyms of company names (2 per match)
• Only use synonyms agreed by 2 mturkers

15. Clean up (4): Alchemy API
• (show output)

16. Clean up (4): Alchemy API
(show output) Text: ‘University of California Berkeley’

17. Clean up (4): Alchemy API
Text: ‘UC Berkeley’
Text: ‘UCBerkeley’

18. Joining synonyms (dplyr left_join)
Combine.final = synonyms collected from mturk

19. Preliminary Result
• Black: Original company names
• Red: After recoding

20. Before recoding
Top 20 companies in People.Co
After recoding Before & After

21. Top 20 companies in People.Co data

22. Thank you!
• Future direction: Merging Compustat data into People.Co data
• Graduate Student: Weiyi Ng and Katherine Ullman
• Faculty sponsor: Professor Toby Stuart
• Lab members: Cindy Mo and Gao Xian Peh
Contact: phoebe.wong@berkeley.edu
LinkedIn: www.linkedin.com/in/wphoebe