This is the first in a series of posts that will provide insight on what we see and where we are going with incorporating job postings into our tools. These posts are reblogged from our US website.
Several years ago, “real-time labour market data” arrived on the scene. The idea behind this data is a good one, using job-posting analytics to shed more light on the labour market by providing current data on employer demand. We have received many questions about when we will add real-time data to our tools, and we have researched this extensively. What we’ve found is that putting this data into practice in a safe, reliable and consistent way is complicated. Any economy is a vast and multi-layered system and every labour market is full of ‘churn’, which is another way of saying that new workers are hired while others have left jobs or switched to a new job. Job postings represent a small subset of that churn and can’t easily be turned into real, solid and actionable numbers. In other words, there are both pros and cons to job postings.
- PROS: Job postings give us specific and detailed information on the types of skills that employers want and who those actual employers are. This means that job postings are a good source of information on skills and companies that are hiring.
- CONS: Job postings are not the same thing as labour market data. They aren’t necessarily real jobs and only represent a small part of total economic activity in the nation. Furthermore, job postings are problematic because they are subject to seasonal shifts and the quality of the data is dependent on scraping company websites. This is like depending on the internet (uncontrolled and unverified) for information versus depending on Government payroll records (more standardised and economically sound).
With all this said, if you are working on a strategic planning project (e.g., gathering information to justify a new course), it is alright to use job-posting data, but only as long as it is used in conjunction with labour market data. Job postings, after all, are only the tip of the iceberg.
Jobs: The Labour Market Perspective
In the United Kingdom, the Government provides the majority of jobs data, which is commonly referred to as “labour market data” or “labour market information” (LMI). This is the main type of data that we use in our products and services. The data comes from agencies like the Office for National Statistics and is used for understanding current employment statistics for more than 500 industries and 350 occupations. So, when we say ‘job’, we are referring to someone currently employed in a specific industry and occupation. People can hold more than one job at the same time, so the number of jobs doesn’t necessarily signify the number of people as much as the number of positions currently held in the labour market.
How is Labour Market Data Generated?
A job in the labour market is associated with payroll records, which companies must report to the Government. The data is released on annual cycles and provides standardisation across geographies so we can gain insight on trends across the country or for different regions. As a result, the data is very useful for big-picture, strategic planning.
The current population of the U.K. is over 62 million, and according to the Office for National Statistics, there are 29.7 million jobs in the U.K. (not including self-employed workers). There are also 2.52 million people unemployed. That is a lot of data to work with and the analysis we provide via labour market data provides insight on all of these jobs. LMI offers us a number of categories of data for each of those millions of jobs, including location, earnings and specific industry, occupation, education level and knowledge and skills level. This data is perfect for understanding present economic realities so we can make better, more objective decisions.
Strategic Planning and the Public Sector
The public sector has been a primary consumer of labour market data for many years. The main users tend to be regional planners (like Local Enterprise Partnerships), colleges, universities and other governmental agencies concerned with the development of regional workforce and education programs. LMI works well in the public sector because the public sector is more oriented to strategic, long-term planning than to near-term, tactical planning.
The primary criticism of LMI is that it is updated too slowly or that it doesn’t cover emerging and in-demand occupations, which has some validity. However, because LMI sources classify so many industries and occupations and have such a rigorous system, the datasets actually do a better job of capturing the total employment picture and revealing present economic realities than most think.
Another Perspective: Job Postings and CVs
While the public sector has been relying on labour market analysis for planning efforts, the private sector has become more aware of and focused on job-postings data. This set of jobs data is all about being more efficient and responsive to short-term hiring and staffing needs. Whereas labour market data is big, standardised and slow, job postings are a veritable Wild West, without standardisation and reacting very quickly to seasonal trends and shifts. For example, every November job postings will tell you that it’s time to get into the retail business and every January they make it appear as though it’s time to shut it down. Job postings are associated with literally thousands of job titles and the list seems to be ever growing.
The data is harvested via web-crawling technology. ‘Spiders’ go out to company websites to find their job postings. Consequently, in the private sector, you will often hear people say, “Company Y has 25 jobs available.” In this case, the word ‘job’ is a shorthand term for job postings. There’s no problem with that, but it is important to know that those postings are an entirely different kind of information than to labour market data. Does that company intend to hire 25 people?
A big pitfall of job-posting data is that any company can make any type of posting for any reason at any time. Meanwhile, a neighbouring company could hire 25 people and not generate one posting. From a data perspective, postings are fraught with difficulty if we are trying to make pure one-to-one connections to the labour market.
When using this data, here are a few important things to note:
- Job postings are not ‘complete’ in the same way that labour market data is. They only capture a very limited number of jobs.
- The data can change a lot from month to month, which can be hard for many public agencies to respond to in a timely manner.
- Postings are very useful for telling us who is hiring, what skills they are interested in and where the most ‘churn’ is occurring for employers right now.
- Try to contact the potential employer to find out if their postings match up with how many they say they are planning on hiring.
Job-Posting Analytics or Real-Time Labour Market Data?
Harmonising job postings with labour market data allows both the public and private sectors can get their hands on more current data to gain both tactical and strategic perspectives. The changing economy and the advent of “big data” as a focus have created a strong desire to better understand all these sources together.
Some in the public sector have begun to refer to the job-posting analytics as “real-time labour market data.” The thought is that the job postings can offer a more current employment picture.
As a company that works primarily with labour market data and has been watching how job-posting information is being used out in the wild, we advise planners to be cautious and perhaps use slightly different phrasing (perhaps ‘job posting analytics’ instead of ‘real-time labour market data’) for four reasons:
1. It is misleading to assume that job postings can be equated to labour market data.
Job postings aren’t real, physical jobs. The data is somewhat theoretical because the company who has the posting hasn’t necessarily hired anyone for that specific position and might not ever hire anyone.
For example, it would be problematic to assume that 1,000 job postings are equal to 1,000 currently held jobs in various occupations or 1,000 positions in a specific industry. And depending on the source, you could be dealing with duplication, especially if the exact same job posting for the exact same job finds its way into 10 or 15 different websites.
Here’s another illustration: an online job search reveals that there are 1,000 jobs for a specific job title in London. Traditional labour analysis of that occupation reveals that there are currently 6,000 total jobs in London. These 6,000 jobs represent actually positions where someone is employed and the 1,000 postings are records of employers looking to fill jobs. If the total market is 6,000 jobs and employers are looking to add another 1,000 new jobs, we would be looking at a 17% increase in employment. Impressive growth! One thousand new jobs is nothing to sniff at. It means companies want to hire and that our example occupation really deserves attention. The point here is that the postings are very different than what we can see from real labour market analysis. We can’t assume that the postings are providing us with the same sort of data.
2. Trend analysis of job postings cannot be compared to labour market trends and performance.
Now let’s say we do a bit more labour market analysis and find that the occupation in question actually grew by only 5% in the past year. That would be 300 new jobs – not 1,000. Over the next year or so, it is probably safer to assume growth closer to 5% as opposed to 17%. It is possible that companies in London want to hire 1,000 additional workers in that year but, as historic LMI indicates, many companies are not able to fill all those positions.
We may have five job postings, but all five positions were filled by five people moving from one company to the next. The gross effect is five hires, but also five separations, with a net effect of zero job changes.”
Also, job-posting activity can be quite high while hiring practices can remain quite steady. When job advertisements are made public, we don’t know if the job is for a new position or to fill an existing position that opened up because someone separated (moved on, retired, etc.). We may have five job postings, but all five positions were filled by five people moving from one company to the next. The gross effect is five hires, but also five separations, with a net effect of zero job changes.
This should cause any planner to pause and review things a bit before decisions are made. If one assumes that a 17% increase in job postings is enough to go on from a planning perspective, that assumption could prove quite expensive if the market is really only expanding by 5%, especially if it results in a decision to add a new training programme.
Again, the job-posting data is useful but misleading if it isn’t nuanced with real employment trends.
3. Job postings aren’t a good measure of employment or the labour market because they aren’t collected via any sort of standardised reporting or coding.
Labour market data is collected officially by the Government and is released at a standardised rate. On one hand, job postings are indicative of hiring trends, skills needs, and other important metrics, but from a planning perspective, these ‘job counts’ are problematic because they only capture data scraped from the internet. Making broad, sweeping decisions based on this data alone is problematic to say the least.
4. Many of the jobs that are available aren’t actually advertised.
This doesn’t mean that job postings shouldn’t be used, but it does remind us that the data is limited. There is a lot of churn going on that job postings don’t capture. A recent article in The Wall Street Journal mentioned the plight of Jessica Rodrigues, a jobseeker in New York City who realized that just responding to online job-postings and trying to network her way into a job wasn’t enough these days. While discussing how Ms. Rodrigues actually found her way into full-time employment, the article mentions an interesting statistic:
While the Internet has made it easy to apply for work, career experts say that offline networking efforts to meet people and get introductions are a far more effective way to land jobs–especially since 80% of jobs aren’t publicly advertised, says Steven Rothberg, founder of job-search website CollegeRecruiter.com in Minneapolis.
How Should We Measure the Economy?
Imagine two people standing in a snowstorm. One of them pulls out a bucket and starts to run around catching snowflakes. This is “real-time,” he exclaims.
Who has the better approach? Well, from a strategic perspective, it is vital to do the actual measurement once things are on the ground — once the jobs are really there. Measuring data on the ground yields much more reliable data, and more accurately captures real job accumulation. Again, the churn that is happening up in the air will in some degree lead to jobs, but it can be very misleading, especially if you are trying to develop a long-term plan.
Daily Forecasts vs. Understanding the Climate
If we were to use current weather conditions (job postings) for decisions that have longer-term impacts – for example “I’m going to the store for a new coat!” – we all know that this might not be the best way to react.
Current weather conditions are useful for answering tactical questions like, “Should I wear a jumper today?” or, “I wonder if I should bring my snow boots?” Nobody (hopefully) uses a weather app to plan a new wardrobe. This would be like using purely tactical data for strategic planning.
However, let’s say we are leaving for Spain for an extended holiday and we need to actually purchase some new clothes. To better understand the sort of investment we need to make we wouldn’t consult a weather app exclusively. We would look at data on typical weather patterns, average temperature and precipitation for the region so we can plan accordingly. Again, this would be tantamount to using labour market analysis for strategic planning.
Conclusion and Review
1. Data is powerful and persuasive, but, as is the case with any powerful tool, it can do more harm than good if it isn’t used in the right way.
2. The word ‘job’ is used in different contexts and can mean different things. It is also associated with an ever-expanding list of data sources, which aren’t all created equal. Therefore, if you use data on jobs and employment to make important decisions – the type that might impact other people – it’s crucial to understand some of the basics of this data before you get too far down the road:
- What sources are you considering?
- Where do they come from and how are they generated?
- How reliable are they?
- What are their limitations and how should they be used?
3. Labour market data is important for understanding fundamental realities of economies and human capital and is more useful for strategic planning. Job postings are great for more tactical responses based on what employers need this month. LMI isn’t going to be as helpful for understanding things like “Company X needs five new welders,” and job postings aren’t going to be as helpful when you wonder whether you should add a new welding programme next year. Remember that by the time the new welding course is up and running and producing graduates, those five job advertisements will have been long filled. The question is whether any increased demand is going to be sustained.
4. Finally, here are a few basic guidelines to take from what we’ve said:
- Understand your terms. Are you talking about labour market data or job postings?
- Does your decision have long-term, strategic ramifications? If so, use labour market data and nuance it with postings to get a sense of who is recruiting.
- Is your decision short-term? Job postings might be just fine.
So, What Is EMSI Doing About It?
Right now EMSI is working on ways to better integrate job postings and labour market data. We feel that information on employers and skills in particular is potentially very valuable. In our next post, we will talk more about the problems we are finding in the data and will shed a bit more light on our approach and what you can expect. Stay tuned!