What labour market data can you use without expensive tools?

You don’t need to spend thousands of euros on licences to recruit more effectively. A great deal of information about supply, demand and competition in the labour market is simply available to the public. The difference lies not in access, but in knowing which data source to use and how to apply it to make practical decisions.

Why publicly available labour market data is often sufficient

Most recruitment questions start with the same uncertainties: is there enough supply, where is it, and what are competitors doing? Publicly available figures often allow you to answer these questions more effectively than relying on gut instinct.

Open data is particularly suitable if you want to start with a data-driven foundation: defining functions, comparing regions, identifying seasonal patterns and testing assumptions. You may sometimes lack detail (e.g. real-time candidate behaviour), but you gain speed and objectivity.

A useful starting point: use data to make decisions on a “smaller” scale. Don’t say: “the labour market is tight”, but rather: “in region X, the proportion of vacancies for role Y has risen, so we need to act more quickly and test a different channel”.

What labour market data can you use without expensive tools?

Listed below are data sources that you can use free of charge or with minimal effort. They can be broadly divided into three categories: macro (economic and sectoral), meso (regional and occupational) and micro (your own process data).

1) CBS: employment, wages, sectors and regions

The Central Bureau of Statistics is one of the most useful sources if you’re looking for facts about employment, wages, economic sectors and regional differences. It’s less suitable for “scraping job adverts”, but very strong when it comes to context: what are the underlying trends?

For recruitment teams, these are typical use cases:

• Issues relating to growth or decline by sector (e.g. manufacturing, logistics, construction).
• Regional labour market: where is employment rising, and where is it falling?
• Wage trends as an indicator of increasing competition for in-demand roles.

A practical starting point is CBS Open Data (StatLine), where you can filter tables by region, period and topic. It’s better to choose one or two indicators to monitor monthly or quarterly than to have twenty charts that nobody updates.

2) UWV: labour market forecasts and labour market tightness by occupation/region

The UWV publishes a great deal of information on high-prospect occupations, labour shortage indicators and regional analysis. This is particularly helpful if you want to know whether your role is structurally difficult to fill in a particular region, and which alternative target groups would be appropriate.

Use UWV data for the following purposes, amongst others:

• Assessing labour market tightness by occupation and region.
• Justifying training or entry pathways (e.g. lateral entry, BBL).
• Managing expectations with hiring managers: “You can’t just find someone with this profile in two weeks‘.

The UWV publishes this information in its labour market reports and regional publications, such as on UWV Labour Market Information. Take a closer look at regional labour market updates if you have a lot of vacancies in a single province or labour market region.

3) In-house vacancy and recruitment pipeline data (often the most valuable dataset)

This is the data source that many organisations underestimate, because it seems “too simple”. Yet your own process data shows you straight away where candidates drop out, what takes time, and which steps predictably lead to recruitment.

Examples of data you usually already have (without any extra tools):

• Number of applications per channel (job board, social media, referrals, our own website).
• Conversion by stage: view → click → application → interview → offer → start.
• Processing time per stage (screening, planning, second interview, offer).
• No-shows and reasons for absence (categorised where possible).

You can often find this information in your ATS, email history and calendar. It doesn’t have to be perfect; a consistent definition is more important than 100 per cent completeness.

4) Competition and demand data via simple SERP and job vacancy checks

Even without paid scraping tools, you can gather insights into the competition. For example: how many similar job vacancies do you see in your region, which employers dominate, and what terms do they use?

Keep it simple and repeatable:

• Select 3 to 5 competitors and check their careers pages once a month for the number and type of vacancies.
• As a rule, note down: job title, location, shift work, salary range (if stated), and unique benefits.
• Take a look at synonyms for job titles: operator A/B/C, production worker, machine operator.

This isn’t an “official” labour market survey, but it does provide useful market intelligence. Above all, it helps you with employer positioning: you can see what everyone else is promising, and where you can credibly set yourself apart.

5) Target group data from interviews and reasons for dropping out

Not all labour market data comes from tables. Qualitative information is also data, as long as you structure it. It is particularly true for blue-collar roles and sales positions that there is a great deal of truth to be found in recurring patterns in objections and drop-out rates.

Examples to be formally recorded:

• Top 5 reasons not to apply for a job (e.g. journey time, shift work, pay, type of contract).
• Top 5 reasons to drop out after the first interview (e.g. pace, unclear role, mismatched expectations).
• Questions that candidates keep asking (often a sign that your job advert or recruitment process is missing something).

This ties in with the concept of candidate journey optimisation: you’re not just improving your campaign, but also the process that follows. A structured approach to selection helps to reliably gather this sort of feedback; see also How to conduct a structured interview in 8 steps.

Which data points are most useful for each recruitment enquiry?

Data is only useful if you know in advance what decision you want to make with it. The table below will help you choose the right source without getting lost.

A simple process: from data to action in 60 minutes

If you want to put this topic into practice, a short, set routine works better than a major investigation. Set aside a block of time each week or every two weeks to answer the same three questions.

Step 1: Choose one vacancy or group of vacancies

Group similar roles together. A shift-based operator is in a different category to a field engineer or an account manager.

Step 2: Select 2 external sources and 1 internal source

Example mix: UWV for labour shortages, CBS for context, and your own funnel data for conversion. This way, you avoid simply telling “market stories” without looking at your own process.

Step 3: Formulate one decision and one test

For example:

• Decision: we’ll reduce the number of steps from 4 to 3 and finalise the plan within 48 hours.
• Test: we’re running two versions of the job advertisement with different titles and opening lines.

This ties in with selecting on the basis of objective criteria. If you get the team to work using fixed criteria, you can learn more effectively from data rather than from opinions. One practical step is to use a scorecard, as set out in Create a selection scorecard for hiring managers.

Pitfalls when working with public labour market data

Open-source software is powerful, but it can also lead you astray. These pitfalls are often encountered in practice.

You’re comparing apples with pears

A job title doesn’t always mean the same thing across different employers. You should therefore define your profile in terms of tasks, context and essential requirements, not just a title.

You expect a level of precision that the source cannot provide

Macrodata is intended to show trends, not to indicate “exactly 73 candidates available”. Use it to help you decide which direction to take, and use your own funnel data for the details.

You do carry out analysis, but you don’t follow it up

Data without action may seem rational, but it doesn’t change anything. If your recruitment is being held back by inertia or subjective decisions, improvement often starts with process and assessment. This also touches on the classic factors that drive up candidate costs; see These 4 mistakes are costing you your best candidates.

The next step if you want to implement this on a regular basis

If you’re starting with public labour market data, choose one job category and build a small dashboard: 2 external indicators (UWV/CBS) and 5 internal funnel metrics. Combine that with regular feedback from interviews and you’ll already have a solid, data-driven foundation without the need for expensive tools.

Would you like help setting up a routine like this, choosing the right data sources and translating this into campaigns and a more streamlined application process? If so, it would be a good idea to have a chat about a data-driven recruitment approach that brings together labour market data, target audience insights and recruitment marketing.