BW #11: Software jobs

Despite the highest inflation many countries have seen in decades, the world economy seems to be doing pretty well overall. Obviously, different countries are seeing different numbers, but the general sense is that we keep waiting for a major recession to hit... and keep waiting.

Many high-tech companies have seen their stock prices go down quite a bit in the last year. Perhaps to bolster their stock prices, perhaps to get ready for a potential recession, and perhaps because they can get away with it, many of them have been laying people off. There's a constant stream of reports about high-tech companies laying off thousands, and sometimes tens of thousands, of employees.

In the good times, programmers who are laid off don't have to worry that much about getting a new job. Right now, though, it feels like it'll be harder.

But is it? Are things really bad right now for programmers who want to find a new job? How much does this differ from other sectors? And do we see a big difference between countries?

While I hear and read about these questions all the time, I was inspired to look into this topic more closely in an issue of The Pragmatic Engineer, another Substack newsletter, which mentioned the data source we’ll be using this week.

Data and questions

This week, we'll look at numbers from Indeed.com, a major job-listing site. Their HiringLab organization grabs data both from Indeed.com and also from elsewhere on the Internet, and then makes aggregate job information available.

The assumption is that most job postings on Indeed.com points to greater demand for workers. All of Indeed's metrics start in February 2020, which is set to be a baseline value of 100.

Note that we aren't measuring the absolute number of job postings! Rather, we're measuring the number of postings relative to February 2020. So if the value for a given week is 100, that doesn't mean there were 100 job postings; rather, it means that we’re seeing the same number as in February 2020.

Also note that this isn’t a direct measure of employment. More job postings doesn’t necessarily mean lower employment, although I’m pretty sure there’s a correlation there.

Our data source will be a GitHub repo created by HiringLab, which they seem to update weekly with job-posting details. If you’re experienced with Git, then you’ll want to download the entire repository with “git clone”:

https://github.com/hiring-lab/job_postings_tracker

If you aren’t familiar with Git, then you can use this URL to get the data as a zipfile:

https://github.com/hiring-lab/job_postings_tracker/archive/refs/heads/master.zip

We’ll be looking at two different metrics — the aggregate data information for the entire US, in the file “US/aggregate_job_postings_US.csv”, and then the per-country information for each job sector. In the US, that information will be in the file “US/job_postings_by_sector_US.csv”.

Here’s what I would like you to do:

• Turn aggregate job postings into a data frame (aggregate_df). We're interested in the date and seasonally adjusted number of job postings, but only where “variable” is “total postings”.
• On how many days since data collection began has the index been greater than 100 (i.e., more postings than in February 2020)?