financial burden resulting from higher benefit amounts and a more significant number of beneficiaries.

# Limitations

## **Despite its usefulness, this study suffers from some modeling limitations.**

**First, estimates of coverage may be interpreted as an upper bound.** Due to limited information in the household survey data, the study did not estimate the factor for some groups: Identifying orphans, children accommodated with another family, or pregnant women 45 days before the term was not possible. Additionally, it is important to note that some crucial parameters of the DMI were not included in the simulation, and this omission may have implications for the overall results. The DMI has different scores if children attend school, and the survey lacks this data for ages between 11 and 13. So, we assumed all children from that age group went to school. Furthermore, additional asset and employability criteria were not included in the model due to insufficient data available from the household survey.<sup>19</sup> . While we have data on the unemployed, specifics regarding their registration with the employment agency are unavailable. The exclusion of these parameters may affect the accuracy and comprehensiveness of the analysis. Moreover, the survey does not comprehensively account for income from capital. Given these additional eligibility constraints, our coverage estimates may be an upper bound.

## **Second, the model does not consider behavioral responses, which may include a higher program take-**

**up or a more substantial disincentive effect** . An increase in the take-up rate of benefits is possible, though the extent of this increase remains uncertain. Encouragingly, higher take-up rates could mean that more eligible households access the assistance they are entitled to, potentially leading to a broader positive impact on poverty reduction. Additionally, the model does not estimate the potential disincentive effects of the MSA reform. The evidence on the disincentive effects of Monthly Social Allowance programs across Europe is mixed. Terracol (2009) reveals that the French guaranteed income program, the RMI, initially exerts a strong disincentive effect, particularly in the first few months of participation. Still, this effect tends to diminish after approximately six months. Moreover, household composition appears to influence how program participation impacts individuals. Coady (2021) delves into the design of means-tested Monthly Social Allowance schemes in various European nations, highlighting the delicate balance between alleviating poverty and managing work disincentives. Many countries prioritize employment incentives over poverty reduction by combining low-benefit generosity with modest benefit withdrawal rates. Lastly, Gouveia (1999) examines the impact of the Portuguese Minimum Guaranteed Income Program (RMIG) and identifies a small, positive effect on reducing inequality and poverty. However, the gains are somewhat offset by labor supply effects.

19 These include restrictions on assets, which are also subject to a test, and registration of the unemployed in the Employment Agency (see Annexes 2 and 3 for detailed eligibility criteria). In the SILC survey data, we cannot identify the registered unemployed, or the number of months this group is registered in Public Employment Services. In addition, we cannot identify whether the number of homes the household owns, and consequently the number of rooms in the first home. It was not possible to use another survey (e.g., Household budget survey) to estimate the likely magnitude of these limitations, as the income-poor from the HBS are not the same income-poor from the SILC. There are sizable divergences in the incidence of income poverty using both surveys.

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