Page 315 - Invited Paper Session (IPS) - Volume 1

IPS155 Emily W. et al.

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INEXDA - The granular data network
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Emily Witt , Stefan Bender , Olympia Bover , Giovanni D’Alessio , Luís Teles
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Dias , Robert Kirchner , Renaud Lacroix , Paulo Guimarães , Manuel Ortega ,
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Lyon Michael , Christian Hirsch 2
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1 European Central Bank
2 Deutsche Bundesbank
3 Banco de España
4 Banca d'Italia
5 Banco de Portugal
6 Banque de France
7 Bank of England

Abstract
The financial crisis of 2007-08 has highlighted the need for using granular data
on financial institutions and markets to detect risks and imbalances in the
financial sector. Data producers such as central banks and national statistical
institutes are witnessing a growing need to improve granular-data access and
sharing. When making granular data available, data producers face significant
legal and technical challenges related to, among others, safeguarding
statistical confidentiality. This paper introduces the INEXDA international
network, which provides a platform for data producers to exchange practical
experiences on the accessibility of granular data, metadata as well as
techniques for statistical analysis and data protection.

Keywords
Microdata, International Network, Data Access

1. The motivation for INEXDA
In 2009, the finance ministers and central bank governors of the G20
endorsed the first phase of the Data Gaps Initiative (DGI-1) to promote actions
to close data gaps that had come to light in the wake of the global financial
crisis that emerged in 2008. During the process of DGI-1, data users and data

compilers increasingly expressed the need for improving data sharing,
particularly of granular data, in order to foster the understanding of global
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1 The views expressed here are those of the contributors and do not necessarily reflect those of
the Banco de España, Banca d’Italia, Banco de Portugal, Banque de France, Bank of England,
Deutsche Bundesbank, or European Central Bank.
2 In this paper, granular data are defined as less aggregated data than traditional statistics (eg
finer breakdowns of aggregates in traditional statistics) or microdata. Microdata are data at the
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