<mark>(2014)</mark><sup>29</sup> <mark>. Second, it incorporates two additional dimensions: education and health</mark><sup>30</sup> <mark>, similar to Alkire and Apablaza (2016) and Weziak-Bialowolska and Dijkstra (2014). An individual is educationally deprived if at least one adult (18+) in the household has only attained primary education (or less).</mark><sup>31, 32</sup> <mark>An individual is deprived in the health dimension if at least one adult (16+) in the household has unmet needs for medical examination or treatment during the last twelve months.</mark><sup>33</sup>

**For the Roma-specific analysis, AROPE indicators are mostly unavailable among ethnic population subgroups (with the exception of Bulgaria) when using the EU-SILC survey instrument.** While other countries may have available ethnic identifiers in other official surveys (such as Household Budget Surveys), the indicators that conform with the AROPE cannot be constructed with other survey instruments.

# **To understand factors associated with multidimensional poverty, a series of Probit models**

**with different specifications were selected** . These were chosen as a suitable statistical approach for modeling dependent variables characterized by dichotomous outcomes, such as the presence or absence of AROPE (at risk of poverty or social exclusion), low work intensity, and severe material and social deprivation. The model includes household and individual-level characteristics. This choice is grounded in its ability to estimate the probability of an event occurring, making it particularly well-suited for binary outcomes. The models were run with different sets of covariates and 2021 and 2022 data to capture the stability of the relationship between a set of explanatory variables and the likelihood of experiencing these specific forms of deprivation and identify any emerging trends or shifts in socio-economic dynamics and the robustness of the results to the specification. We report marginal effects, which allows for a clearer interpretation of the model results by quantifying the change in the probability of the outcome variable associated with a one-unit change in the independent variables. This facilitates a deeper understanding of the factors associated with the likelihood of being at risk of poverty or social exclusion, facing low work intensity, or experiencing severe material and social deprivation, thereby informing targeted policy interventions aimed at alleviating these socio-economic challenges. Annex 4 describes the specifications of the regressions performed.

**To identify at-risk groups, we compare unconditional and conditional AROPE means. In our modeling approach, we incorporate several vulnerable groups**<sup>**34**</sup> **as covariates to assess whether they continue to face a higher probability of experiencing poverty, even when controlling for other observable characteristics.** By including these covariates in the probit model, we aim to examine the unique contribution of each vulnerable group to the likelihood of being at risk of poverty or social exclusion, facing low work intensity, or experiencing severe material and social deprivation. This allows us to identify persistent disparities in socio-

> 29 We do not include other services and infrastructure variables (such as quality of the dwelling or sanitation facilities) typically included in the EU SILC because these variables are not available in the 2021 and 2022 surveys.

> 30 We do not include self-reported environmental variables (noise from the neighborhood, pollution, crime) because these are not available in the 2021 and 2022 surveys. They were collected annually until 2020 and then again in 2023. 31 We use primary education as the deprivation threshold because the majority of the population has secondary education or above. Thise with other studies on Europe, including those of OPHI.

> 32 We could not construct a measure of educational deprivation among children because the SILC survey is not designed to collect educational attainment data for children. Therefore, enrollment rates may vary significantly from administrative data. Thus, we rely on information on the educational level of adults (Alkire and Santos, 2014). 33 In the EU-SILC, the question of unmet needs for medical attention or treatment is only asked for individuals aged 16 and over. Given this, for the educational and health variables we use the same age cutoffs used by Eurostat.

> 34 These vulnerable households were selected following previous evidence on poverty profiles and households vulnerable to shocks in these countries (Prasad et al., 2023; World Bank, 2017, 2018b, 2021, 2023a, 2023b).

12