From annual to quarterly data: challenges and strategies in the estimation of Italian General Government Compensation of employees

Sara Cannavacciuolo, Maria Saiz and Maria Liviana Mattonetti

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Abstract: This paper addresses the methodology for the quarterly estimation of Compensation of Employees paid by the General Government (GG) sector, in accordance with the European System of Accounts (ESA 2010). Due to the limited high-frequency data availability and the need to guarantee the consistency with annual constraints, quarterly estimation relies on indirect temporal disaggregation techniques. These methods use specific infra-annual indicators as proxies for the variables being estimated. The specific case of the quarterly estimation of Compensation of employees presents several additional challenges. Firstly, the information provided by the sources, based on cash or legal-accrual data, is elaborated to define indicators which respect the accrual ESA 2010 principle as the annual estimates, based on more compliant data sources such as final budgets of public entities. Secondly, at a quarterly level the extraordinary events - such as the recording of delayed collective bargaining agreements which result in arrears - have a strong impact on quarterly indicators, whereas their effect is mitigated at annual level. To attribute these flows to the period when the work is performed, multi-source data harmonization techniques are employed. Thirdly, to accurately reflect intra-annual dynamics, information is collected for specific groups of GG entities (e.g., regions and provinces) and aggregated into ESA 2010 GG sub-sectors (Central Government, Local Government, Social Security Funds) leading to three specific estimates. To validate temporal disaggregation models and ensure methodological rigor and data quality, statistical tests are applied throughout the process. The results confirm the effectiveness of this methodology in providing accurate and timely quarterly estimates of Compensation of employees for the GG sector, thereby supporting reliable short-term economic analysis and policy making.

Date: 2026-01
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