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IPS246 Tiziana Laureti et al.
therefore, essential that the indicators used for measuring poverty are of the
necessary high quality, in particular in terms of their accuracy, timeliness,
usability and their reliability. In this context, there is also an increasing demand
for estimates of poverty and social policy indicators at regional or sub-national
level (NUTS 2, and exceptionally NUTS 1) to be used for benchmarking and
assessing the efficiency of regional policies (Piacentini, 2014).
Therefore, in this paper we present an empirical evaluation of uncertainty
in measuring well-being at local level, a crucial aspect that impacts both the
design and evaluation of policies. We focus on the accuracy of at risk of
poverty rate (AROP) which is estimated from the European Union Statistics on
Income and Living Conditions (EU-SILC) survey.
After reviewing the literature devoted to the computation of standard
errors of traditional measures of monetary poverty and to the assessment of
non-sampling errors, we estimate AROP standard errors for Italy taking into
account the complex survey design and using various approaches, such as
linearization and re-sampling methods. Moreover, we provide estimates of
standard error of AROP at regional level (NUTS-2) in order to better inform
policy making. We use the 2017 wave of EU-SILC for Italy in which detailed
information on the sample design are provided by the Italian National
Statistical Office (ISTAT) thanks to a research project carried out within the
Dagum Inter-Universities Research Centre on Advanced Statistics for the
Equitable and Sustainable Development.

2. Methodology
2.1 Measuring uncertainty for economic well-being
Over the last decades, there has been increased interest in comparative
analysis of poverty and social exclusion in the EU both at national and at
regional level. In 2004 Eurostat launched the EU Statistics on Income and
Living Conditions in order to create a European standardized database to
generate comparative measures of poverty and social exclusion among the
Member States. In the framework of Europe 2020 strategy of the European
Commission, estimates of poverty and social exclusion at sub-national
regional level have become of central interest since local differences in
poverty are essential for policy decisions and monitoring (Betti et al, 2012;
Piacentini, 2014).
At the same time, Eurostat and a number of stakeholders are reflecting
on ways to further improve the quality of statistics on income and living
conditions based on EU-SILC survey, especially regarding their accuracy
(Atkinson, 2017).
In this context, the issue of measuring the uncertainty of the poverty
indicators that have been officially adopted by Eurostat has become

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