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Interpreting changes between point estimates at different waves may be misleading if we do not take the sampling variation into account. It is therefore necessary to estimate the standard error of these changes in order to judge whether or not the observed changes are statistically significant. This involves the estimation of temporal correlations between cross-sectional estimates, because correlations play an important role in estimating the variance of a change in the cross-sectional estimates. Standard estimators for correlations cannot be used because of the rotation used in most panel surveys, such as the European Union Statistics on Income and Living Conditions (EU-SILC) surveys. Furthermore, as poverty indicators are complex functions of the data, they require special treatment when estimating their variance. For example, poverty rates depend on poverty thresholds which are estimated from medians. We propose using a multivariate linear regression approach to estimate correlations by taking into account the variability of the poverty threshold. We apply the approach proposed to the Turkish EU-SILC survey data

Variance Estimation of Change in Poverty Rates: an Application to the Turkish EU-SILC Survey