Actual individual consumption (AIC) measures household material welfare. In 2024, ********************* recorded the highest AIC per capita expressed in purchasing power standards (PPS) in the CEE region. However, all Central and Eastern European countries reached actual individual consumption per capita below the EU average.

In 2024, no country from the CEE region recorded actual individual consumption (AIC) per capita measured in purchasing power standards above the EU average. However, the highest AIC was in Lithuania (12 percent below the EU average); the lowest were in Hungary and Bulgaria (30 percent below the EU average).

This paper provides a European-level analysis using a large-scale survey of 13 countries to examine the power of relevant economic and socio-demographic characteristics to account for changes in food consumption and purchasing behavior during COVID-19. This was done by focusing on a two-level analysis of subject-related predictors highlighted in many existing country-level studies to test the generality of their significance. The Level 1 predictors relate to the individual households participating in the survey consisting of household composition, education, and location, as well as three types of perceived COVID-19 risks of infection, severity, and anxiety. Level 2 relates to the national level, and especially to the financial situation measured by the mean national Actual Individual Consumption (AIC) per capita in PPP, of the countries, in which the households reside. In terms of changes in food consumption, results show that household composition, education, and the household’s perceived risk of both being infected by COVID-19 and being severely infected are significant predictors, although there are some differences between the two levels. Some possible explanations are as follows: putting food into one’s body in the context of the pandemic is related to a household’s financial situation, its composition, especially the presence or absence of children and older people, and its educational attainment, and through all these aforementioned to the perception of COVID-19 infection and its severity risks. Changes in food purchasing react significantly to the same predictors, but additionally, to all other predictors at both household and AIC levels. The household’s location and perceived COVID-19 anxiety risks are thus also significant. Food purchasing depends much more on factors operating both at the individual household level and the AIC level together; for example, households’ access to food is affected by both national and local lockdown restrictions that vary according to the location of the household.

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The abbreviations C-H, C-M and H-M refer to the cases to hospitalizations, cases to mortality and hospitalizations to mortality scenarios respectively.

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This dataset contains detailed results of the joint Eurostat-OECD Purchasing Power Parities Programme for the 48 participating countries from 2020 onwards. The production of PPPs is a multilateral exercise involving the National Statistical Institutes of the participating countries, Eurostat and the OECD. Indicators with OECD as reference and with the European Union as reference are presented in this dataset.

   <p>Data until 2021 is final, 2022 is quasi-final, and 2023 is preliminary. The next update will include:</p>
   <ul>
   <li>First estimate for PPP for GDP 2024 in March,</li>
   <li>Update for PPP for GDP, AIC, and HFC 2024 in June,</li>
   <li>Update for all analytical categories, 2022-2023 and 2024, in December.</li>
   </ul>

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AIC values for patch occupancy models of two bird species at 15 lakes. Models allowed the probability of occupancy (ψ) and the probability of detection (p) to vary with human disturbance. The best models are in bold.

AIC for fitted models.

AIC, BIC, AICc and KS p–values (as defined in Section 3) for the fitted distributions.

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