The Independent Authority for Fiscal Responsibility (AIReF), the Tax Agency and the National Social Security Institute (INSS) have signed an agreement that will allow the institution to access a comprehensive set of information, appropriately anonymised, which is essential for an accurate analysis of the efficiency and effectiveness of the Minimum Living Income (MLI). Specifically, AIReF requires access to data derived from management by the INSS and to income and wealth data from the Tax Agency.
As laid down in the Law establishing the Minimum Living Income, the result of the measure and the various inclusion strategies and policies will be evaluated annually by AIReF through the issuance of the corresponding opinion.
AIReF is currently preparing the first Opinion on the MLI, which will address its potential capacity to reduce poverty or its intensity, together with the minimum incomes of the Autonomous Regions. It will also evaluate the results achieved so far and the aspects relating to its management and implementation, including a study of the potential beneficiaries who have not yet received the benefit.
The agreement, which has been processed within a reasonable timeframe given its complexity, was signed on March 26th and provides a stable solution for at least four years to meet AIReF’s needs in order to fulfil the mandate set out in the Law. The agreement guarantees due security and confidentiality of the data to be transferred.
The signing of this agreement also demonstrates the usefulness of such agreements to efficiently and effectively meet AIReF’s needs, as this institution has been pointing out for some time.
The advantages of having the files for the full year and the tax information for the immediately preceding reference year mean that the successive publications of this opinion need to be moved to the second quarter of each year. For this reason, AIReF has agreed to postpone the publication of the first opinion on the MLI to the second quarter of 2022, following the signing of this agreement, which allows in-depth analysis of the data.