As part of my work, I get to speak with people from all over the world. And they all have been telling me, for at least a year, that the job market feels pretty tough. Companies don't have as many openings as they used to, and are taking their time in hiring even when they do have an opening. Young people have seemed especially nervous, given all the talk about companies not wanting to hire junior engineers when they can hire senior ones who have the experience to use AI.
But of course, that's all anecdotal data. Are things really bad? Are they getting worse?
This week, we'll look at hiring data, as collected and published by Indeed's Hiring Lab (https://hiringlab.indeed.com/). They share daily updates on the number of total job postings, as well as new job postings. By looking at these trends over time, we can start to understand the situation, in the US and in other countries.
Data and five questions
This week's data comes from the Indeed Hiring Lab, which publishes its data on GitHub (https://github.com/hiring-lab/job_postings_tracker) under a Creative Commons license. We'll be looking specifically at their "Jobs postings tracker" data, which includes daily updates on the number of total and new job postings. The report does not specify how many postings there are. Rather, it uses a number to show the relative number and growth in the job market.
The number of postings in February 2020 is taken as the baseline, and is labeled 100. A day with more job postings than were available in February 2020 has a number higher than 100, and one with fewer postings has a number lower than 100. So while we cannot use these numbers to know how many job postings there are, we can know whether things are trending up or down.
The GitHub repo includes a number of different related data sets. We'll explore several of them.
Paid subscribers, both to Bamboo Weekly and to my LernerPython+data membership program (https://LernerPython.com) get all of the questions and answers, as well as downloadable data files, downloadable versions of my notebooks, one-click access to my notebooks, and invitations to monthly office hours.
Learning goals for this week include working with multiple CSV files, dates and times, window functions, pivot tables, and plotting with Plotly.
Here are my five questions and tasks for this week. I'll be back tomorrow with my solutions and explanations:
- Read the US aggregate job postings into a Pandas data frame. Create a line plot showing the mean monthly values for both total and new job postings (seasonally adjusted). How is the recent job market compared with February 2020? Do things appear to be improving or declining?
- Look again at US aggregate job postings, at the mean monthly values for total (seasonally adjusted) job postings. Create a bar plot showing, per year, the number of months with positive changes, minus the number of months with negative changes (vs. the previous month).