Dashboard: https://jobmarketanalytics.com/#months=%2212%22&technology=%...
Source Code: https://github.com/petracarrion/job-market-analytics
Slide Deck: https://petra.carrion.io/job-market-analytics/slides/
Important! If it is offline, please refer to this image: https://gcdnb.pbrd.co/images/ElMG6yee19KT.png?o=1
I talk to HR people from different companies (in Berlin) from time to time and they still complain about the lack of skilled workers. Lastly, there is an armada of economic institutes in Germany that do not foresee a "job market crash" either. Jobs are a lagging indicator and the German economy is still growing. Only next year will there be a slight recession.
P.S.: For a good statistical analysis, the data source should be mentioned (which website), different sources (e.g. different portals, different source). A data lake otherwise does not make much for an alarmist analysis.
There could be something else of course. But also possible that some companies are being hit by energy crisis.
I'd like to see monthly numbers for at least a couple political cycles, or better yet for several stock market cycles.
How do you know that?
https://www.tagesschau.de/wirtschaft/konjunktur/eu-kommissio...
EU commission thinks, it will be a loss of 0.6 %.
First of all, judging by the overview chart the decline is steep, but it is currently roughly where it was a year ago. Given that we don’t have a huge tech ecosystem in Germany, most people aren’t employed at tech companies, they are at traditional companies. And for them it’s not that uncommon to see less newly opened positions towards the end of the year. Given that you just started scraping a year ago that seasonality hasn’t been captured so far, but I would expect the job portals data to support this.
Beyond that, many companies dramatically increased hiring in the later (post-) Covid area. It would not surprise me if some companies over hired given the currently harsh economic environment. Since laying off people isn’t as easy as it is in the US, you stop hiring to make up for it.
Also, given my limited experience from the hiring side of things, the demand still seems to outpace the supply of reasonably qualified individuals. According to Statista, Germany had 15k CS graduates in 2021 [1] and turnover of experienced employees is usually relatively low. So considering this and the already declined >100k total openings is still not a real reason to worry (at least based on this data)
[1] https://de.statista.com/statistik/daten/studie/789519/umfrag...
I'm very suspicious of the data, because a 33% decrease in less than a month is unreasonable. Even at the height of Covid, there wasn't that much of a drop-off.
My money is on the script being broken.
November looks more like a recovery month compared to a very bad September and bad October.
Hiring will probably pick up again next year, though. I hope you find something.
My recommendation is a Master's degree if you really want to stay in tech.
About whether these trends that we see in the graphs are something real or just a data issue like seasonality or a problem with the data sources, I think we will soon know from the mainstream media. As always the time will tell.
1) What is your source? - the slides only state it's the biggest platform. 2) How do you compensate, that your dataset only represents x% of the real offers? 3) If you think you have a reasonable share of the job offers, how do you justify this assumption?
These are the main questions, that I would ask if you tell me to use it as business information - all the technical stuff is secondary. But there are more:
4) How do you compensate a change in recruitment strategy? You are just looking at one platform. 5) What makes you sure, that every item in your dataset is interpreted correctly? Can you somehow make sure, that your parsing and reformating is always correct? Can you verify that? Can you detect if not? 6) Can you detect a change on the website itself, e.g. new categories, differences in job description?
Also my experience is different, with getting a ton of job offers currently, directly via business network, I'm willing to look at a statistics based bigger picture that changes my mind. But the interpretation and the harsh change, together with these open questions make me believe that you more likely have a technical problem. So my advise would be, spend a lot more slides on the data and not the acquisition, because all acquisition effort is useless if you can not verify your data and results.
Is there some German/EU law that requires job postings to be available for at minimum one calendar month? Cause that spike at ~30-31 days is real conspicuous.
Or at HR people just defaulting to 30/60/90 days for the posting being automatically removed?
That means that the crash probably started in October and we started seeing it on the data at the beginning of November.
I guess a large majority of those postings go unfilled.
Do you do any work to try to find if a position has been re-posted and recombine it with its previous posting for the purpose of this calculation?
I say that since they seem to remove them (presumably because they got enough applicants or actually hired someone) before the 30/31 day mark.
> the major job offer portal in Germany for over one year
I hope that's Linkedin. Because if it's another site like Monster, StepStone or similars, I'm afraid the data is probably garbage. I don't know a single engineer in Germany who uses anything but Linkedin (well, many engineers use niche job boards like remoteok, or directly use the company's career section... but no single one uses Monster/StepStone/Glassdoor, because they are total crap)
Edit: the data source seems to be stepstone.de. That's unfortunate. Is there any chance to run this against Linkedin for 2023? That would be awesome!
(https://github.com/petracarrion/job-market-analytics/commit/...)
That being said, with the data you have available here, you can’t conclude anything at all - you would need several years of data to rule out seasonalities, and also take into account some external factors influencing it.
Ps. Are you sure you’re not using something like rolling average and you just don’t have the data points (if it’s centered around current date) ?
That's the point of time series -- you need to accumulate them over long periods of time and then you start processing them to understand them better to see if they are leading/contemporary/trailing predictors of what you really care about -- in this case, the tech job market.
So keep it up! But also wait a bit before using it :)