Lithuania

SPI Country Brief

The Statistical Performance Indicators measure the capacity and maturity of national statistical systems by assessing the use of data, the quality of services, the coverage of topics, the sources of information, and the infrastructure and availability of resources. The goal is to improve development outcomes and track progress toward the Sustainable Development Goals.

The hope is that the SPI framework will give countries incentives to build better statistical systems and will help create data ecosystems that can develop and adapt to the requirements of governments and citizens - so that better data can support better decisions.

This is the Country Report for Lithuania based on data from 2022.

SPI overall scores provide an overall summary of the performance of statistical system. This chart shows the overall scores across countries. Scores range from 0-100.

The SPI allows the tracking of indicators over time. SPI overall scores are available since 2016.

The SPI overall score is based on five pillars of statistical performance: data use, data services, data products, data sources, and data infrastructure. The scores for each pillar are shown in the figure.

Data Use: Statistics have value only if they are used. A successful statistical system produces data that are used widely and frequently. Scores for specific indicators are on a scale of 0-1, with 1 being the best possible score.

Data Services: A range of services connects data users to producers and facilitate dialogues between them, thus building trust and adding value to data..

Data Products: The feedback systems between data producers and data users drive the design and help increase the range of statistical products available, and can help improve their accuracy, timeliness, frequency, comparability, and levels of disaggregation. The availability and quality of key NSS data products signal whether countries can produce indicators needed to measure progress toward the 17 Sustainable Development Goals.

Data Sources: To create useful products, a statistical system needs to draw on sources inside and outside the government. Modern data collection thus goes beyond the typical censuses and surveys to include administrative and geospatial data, as well as data generated by private firms and citizens.

Data Infrastructure: A mature statistical system has well-developed institutional infrastructure (legislation, governance, standards), soft infrastructure (skills, partnerships), and the financial resources to deliver useful—and widely used—data products and services.

Underpinning these five pillars are 22 dimensions and 51 indicators. Please visit the framework page of the SPI website for more details or read our journal article in Scientific Data.