Assessing the Accuracy of Multisource Register-based Official Statistics for Multinomial Outcomes

TL;DR

This paper introduces a new, interpretable error measure for multisource register-based statistics, validated through an application to Italian education data, enhancing accuracy assessment in modern statistical systems.

Contribution

It proposes an analytical, flexible global error measure for multinomial outcomes in multisource register-based statistics, addressing multiple sources of uncertainty.

Findings

The measure accurately approximates global error in mass-imputation procedures.

It is computationally feasible and adaptable for on-the-fly statistics.

Application to Italian education data demonstrates practical utility.

Abstract

The emergence of new data sources and statistical methods is driving an update in the traditional official statistics paradigm. As an example, the Italian National Institute of Statistics (ISTAT) is undergoing a significant modernisation of the data production process, transitioning from a statistical paradigm based on single sources (census, sample surveys, or administrative data) to an integrated system of statistical registers. The latter results from an integration process of administrative and survey data based on different statistical methods, and, as such, prone to different sources of error. This work discusses and validates a global measure of error assessment for such multisource register-based statistics. Focusing on two important sources of uncertainty (sampling and modelling), we provide an analytical solution that well approximates the global error of mass-imputation procedures for multi-category type of outcomes, assuming a multinomial logistic model. Among other advantages, the proposed measure results in an interpretable, computationally feasible, and flexible approach, while allowing for unplanned on-the-fly statistics on totals to be supported by accuracy estimates. An application to education data from the Base Register of Individuals from ISTAT's integrated system of statistical registers is presented.