but also tax policy and public spending, as well as the interaction between these policies. In contrast, individual microsimulations focus only on a single program or policy and do not account for the broader fiscal context. Furthermore, the CEQ approach evaluates the combined impact of different policies on poverty and inequality, which is especially important given that many households receive multiple social transfers and are affected by multiple tax policies. By analyzing the entire fiscal system, policymakers can identify where the system is failing to address poverty and inequality adequately and can design more effective policies to address these challenges.

**Given the purpose of this paper, we refine and improve the modeling of social transfers to better identify social program beneficiaries using eligibility conditions.** In the 2018 CEQ (Vaughn & Cabrera, 2022), most social protection transfers were assessed using the direct identification method, an important limitation for assessing social transfers **.** This method primarily relies on the responses provided by households in the survey concerning social exclusion transfers. However, these responses typically lack the granularity needed to discern the exact nature of the transfers received. In other words, the dataset does not specify whether the reported transfers pertained to the heating allowance, the Monthly Social Allowance (MSA) scheme, or other non-contributory, means-tested programs.<sup>15</sup> . Furthermore, when comparing the reported figures related to social exclusion transfers, a significant discrepancy emerged between the total expenditure calculated from the dataset and the figures reported in the Survey on Income and Living Conditions (SILC). This discrepancy further complicates the accurate assessment of the effectiveness and impact of various social protection programs.

**In addition to identifying individual social programs, using self-reported information on household surveys to capture beneficiaries of social programs may lead to bias due to measurement error, as documented in the literature.** Survey data capturing social benefits, such as the SILC data, often undergo harmonization processes. Still, it may exhibit bias because lower-income households tend to underreport the benefits they receive. This underreporting is typically attributed to difficulties in recalling specific benefit details or a reluctance to disclose such information due to social stigma or other factors, such as survey respondents confusing social benefits with earnings (Bargain et al., 2012; Figari et al., 2012; Lynn et al., 2004; Bollinger & Tasseva, 2023, Meyer, 2019). Survey underreporting of transfer programs carries significant implications for our analyses of low-income populations, as this bias may result in an underestimation of the actual level of support offered by Minimum Income (MI) programs. Relying solely on survey data markedly understates the income of poor households, which could lead to a skewed and inaccurate representation of their welfare. Moreover, it can distort our comprehension of how effectively these programs are targeting those in need, and, perhaps most importantly, it can significantly downplay the actual impact of anti-poverty initiatives. In essence, survey underreporting undermines our ability to

> 15 In the SILC, mean-tested noncontributory programs are captured in the variable HY063 (means-tested, noncontributory social exclusion not elsewhere classified). These include means-tested social assistance (GMI),  heating allowances, and other benefits aiming to support low-income families with children, including the monthly child allowance, the monthly allowance for raising a child under the age of one, targeted allowance for schoolchildren enrolled in 1st grade at a state or municipal school, and other non-contributory and means-tested benefits including a lump-sum pregnancy grant for uninsured mothers and financial support to prevent the abandonment of a child or bringing up of a child by relatives or foster family.  The GMI and the heating allowances cannot be directly identified from the household survey. Therefore, these benefits need to be simulated in the survey using eligibility conditions and administrative data on beneficiaries.

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