Use of data sources and analyses
As a research agency, we are accustomed to working with large datasets from a wide range of sources. We analyse, combine and link data to arrive at actionable conclusions. We apply data analysis for purposes such as:
- Impact studies: analysing various data sources to calculate economic and societal effects. These effects may result from policy or from externally driven (exogenous) developments. Such effects often play a central role in cost‑benefit analyses (CBA/MKBA) or policy evaluations.
- Forecasts: by combining data with scientific insights, we map expected developments. For example, we forecast the housing demand of international workers or the capacity needs of international schools.
- Visitor analyses: including visitors to events, city centres, tourist areas or attractions.
- Mobility analyses: linking mobility data provides insights we use in our CBAs. This may involve estimating cycling potential, changes in modal split, the effects of disruptions, or travel time impacts.
- Broad Prosperity analyses: monitoring the regional state of broad prosperity, trends and developments, and identifying challenges and opportunities down to neighbourhood level.
- Economic structure analyses: understanding how areas function economically — for example, airports, business parks, municipalities or regions. Or analysing how an economic sector operates by examining sector structure, added value, indirect effects or firm size.
Data sources
We are well‑versed in navigating a wide range of paid and publicly available data sources, including:
- CBS Microdata (custom analyses based on CBS source data)
- CBS StatLine (publicly available CBS statistics)
- CBS Regional Monitor of Broad Prosperity
- Employment registers such as LISA, PAR and UWV
- Real estate data
- Housing transaction price data
- Mobility data such as traffic volumes, public transport smart‑card data, commuting flows, traffic models, accident statistics, and data from vehicles or mobile systems (including floating car data)
- Visitor data based on hotel stays, counting points or GSM data
- Passenger and freight transport data: inland shipping data (IVS), air freight data, Schiphol data and customs counts
When required data is not yet available, we are able to generate our own data sources. Together with our research partners, we use methods such as:
- Online surveys
- Panel research
- Fieldwork
- Digital tools such as web scraping (automated collection of website data), WiFi or camera counts, etc.
We handle all data sources confidentially. Sensitive information is always presented in aggregated form so that it can no longer be traced back to individual persons or organisations.
Analytical instruments
We analyse and present collected data using a wide range of tools. From descriptive analyses in Excel to more complex analyses and modelling in R, where data is transformed into models to uncover patterns. A selection of our tools and expertise includes:
- Economic statistics: collecting, organising and analysing economic data to clarify economic developments — such as growth, unemployment or inflation — in specific areas or sectors. Interpretation of figures is central here.
- Econometric analyses: examining relationships between economic variables using statistical methods. This enables us to calculate willingness to pay or price elasticities. We use various regression and related methods, such as OLS, Difference‑in‑Difference, RDD, PSM, discrete choice models, the Hedonic Pricing Method, and forecasting techniques.
- GIS analysis: using geographic information systems to create spatial analyses, which we translate into visually appealing maps.
- Data visualisation: creating clear and engaging graphs, maps and tables using tools such as QGIS, Power BI, Excel, R and PowerPoint.
