FSO's Experimental statistics
Experimental statistics are produced using new methods and/or new data sources and are therefore in line with the FSO’s data innovation strategy and the Confederation's multi-annual programme for federal statistics. This site contains descriptions of the (pilot) projects currently being developed.
By publishing them we can involve users and partners at an early stage for both the development and consolidation of projects. The aim of these statistical projects is to better meet users’ needs in terms of efficiency, quality and speed.
However, these statistics still have the potential to evolve, especially regarding their methodology, which is still being assessed. For this reason they are clearly marked as experimental and carry a logo that can easily be recognised.
Projects
Projects
List of projects that are active or have been transferred to production.
Project currently under development / ongoing
Poverty measurement integrating wealth (AssetPov)
In future, the poverty statistics are to be supplemented with an indicator based on households’ total financial means (income and assets). The FSO therefore integrated a pilot module on the topic of wealth in the survey on income and living conditions (SILC), which is also the basis of data for official poverty statistics.
Revenue from accommodation in the hotel sector (HESTA-Mon)
These experimental statistics provide insights into hotel sector revenue.
Booking platforms data (Eurostat)
In 2020, the European Commission signed a data sharing agreement with four platforms - Airbnb, Booking.com, Expedia Group and Tripadvisor. This allows Eurostat to publish new data on short-term accommodation.
Household typology (STATPOP)
The aim of this project is to obtain a “household type” variable for all private households in the permanent resident population at the main place of residence.
Highest level of education completed (STATPOP)
The aim of this project is to attribute the highest level of education to the entire permanent resident population.
Model-based estimation of the gross value added in the enterprise groups statistics (VAB-STAGRE)
The aim of this project is to develop the gross value added (GVA) of business groups using “Small Area Estimates” (SAE) methods. These estimates cover the overlap between the Enterprise groups statistics (STAGRE) and value-added statistical survey (WS) populations.
Mortality monitoring (MOMO)
The FSO's mortality monitoring system is designed to detect whether the weekly number of deaths is higher than expected at that time of year. Mortality rates are an important indicator of health statistics and are of fundamental importance for public health.
Estimation of value added and related economic indicators using small area estimates (SAE_WS)
This project aims to leverage small area estimation (SAE) techniques to develop estimates based on data collected through the Federal Statistical Office's (FSO) value-added statistical survey (WS).
Early estimate of Swiss business structure statistics (STATENT-Flash)
Die Statistik der Unternehmensstruktur wird jährlich rund zwanzig Monate nach dem Referenzjahr veröffentlicht. Mit diesem Projekt sollen basierend auf einer Schätzung die wichtigsten Ergebnisse zur Unternehmensstruktur rascher bereitstehen.
Projects transferred to standard production
Labour productivity by economic activity
Labour productivity by economic activity is published annually as part of the Growth and Productivity Statistics (WPS).
Area Statistics Deep Learning (ADELE)
The FSO’s land use statistics are an invaluable tool for long-term land observation.
Automation of NOGA coding - NOGAuto
Automation of the coding of the economic activity of enterprises using Machine Learningmethods applied to data already available within the FSO (data from surveys, descriptionsin the commercial register, keywords, explanatory notes for classifications etc.) to supportcoding.
Construction of an income from employment by data linkage and application to holders of vocational education and training certificates (LABB)
Analysing the evolution of income from employment in the first years after obtaining a qualification through the Swiss education system is key to assessing how well the education and training system caters to the labour market.
Machine Learning SoSi (ML_SoSi)
The clustering of typical prospective trajectories patterns concerning the receipt ofbenefits in the social security system as well as employment and the estimation of clusteraffiliation through the use of individual characteristics and retrospective process dataapplying a machine learning approach.
Data validation with machine learning
The aim of this project is to extend and speed up data validation in the FSO by means of machine learning algorithms and at the same time to improve data quality.
Experimental statistics in Europe
Experimental statistics in Europe
Various other countries in Europe are experimental statistics based on new methods and new data sources. All the countries of the European Statistical System (ESS), which currently propose experimental statistics can be found here.
