hodsonjames / employment Goto Github PK

Thoughts:

I find it really difficult to understand how the paper is piecing everything together to build the desired algorithm but I did find all the code online. I think it would be more efficient to understand this research by analyzing how the code works.

Questions:

1. What is the role of compensating differential in this research
2. What is an idiosyncratic shock?
3. On page 30, what does it mean by an idiosyncratic draw from a type I distribution.
4. Is the ultimate goal to calculate firm values for each firm from M, the mobility matrix?

Summary (still under construction):

1. Construct M, the mobility matrix, for detail refer page 1368

Steps?

2. Compute firm values from M. How???

Steps?

Hi Honghao,

We would like to investigate how good the data coverage actually is (what percentage of individuals employed at a certain firm we capture), and how that has changed over time. To this end, could you please do the following?

1. Out of all the firms that we have looked into so far, take those that are publicly traded.
2. Download the historical numbers of employees (let's say going back to 1990) from Compustat, which you can access through WRDS. By the way, do you have access to WRDS through Northwestern?
3. Compare the number of employees that we see in our data each year against the numbers Compustat. What fraction of employees do we capture for each firm? How does that fraction change over time?

Please let me know if you have any questions.

Thank you,
Anastassia

Also, break these down by the following attributes available in the data: (a) birth year/buckets, (b) secondary skills, (c) education level/eliteness/major/department, (d) gender, (e) country, (f) next job industry, (g) others you think might be interesting.

Write a script to read/aggregate the employment files to produce stats (and charts) for month/year trends in hiring/firing of accountants at PwC and Deloitte separately.

Hi Honghao,

We'd like to be able to see more granular details about the types of jobs and company structures for the companies we are analysing. So, we need to annotate some of the job title data to allow us to see these features more consistently.

1. For each industry of focus (tech, banking, Pwc/Deloitte), could you group the top 1,000 job titles by frequency. Then for each of these job titles, assign a department where it makes sense and one is not already present.

2. For each of these job titles, please go through and assign the following labels to allow comparison of seniority within/across firms: [Entry-level/Junior - 1, Experienced - 2, Team Management - 3, Middle Management - 4, Top (Regional/Departmental/Executive) Management - 5].

We would then like to add plots by seniority and department, to see how these firms are changing along these dimensions over time.

Thanks!
James