ification, payment). These data have been extensively used and validated in empirical research using data on public finances in Brazil (Gadenne, 2017; Corbi et al., 2019; Shamsuddin et al., 2021). Moreover, the federal government performs several checks to guarantee an adequate level of quality. Our data is much more granular than SICONFI, measuring individual commitments, verification and payments, but once we aggregate at levels such as municipality-year we should expect to match totals from SICONFI. A natural validation of the quality of our dataset therefore is to compare our aggregates with those provided by SICONFI.<sup>20</sup> We perform the following exercises. First, we aggregate both amounts committed and paid at the municipality-year level in our new dataset and compare these values with information from SICONFI.<sup>21</sup> Formally, we compute Dmt = (Tmt<sup>BE−T SICONFI</sup> mt )/Tmt<sup>SICONFI</sup> , where Tmt<sup>BE</sup> represents total expenditures for municipality m and year t as calculated from our budget execution data and Tmt<sup>SICONFI</sup> represents total expenditures as calculated from SICONFI data.<sup>22</sup> In Figure 3, we present the histogram of the percentage deviation of _committed amounts_ from SICONFI, across states. Our key takeaway is that for five states (CE, MG, PB, PE and SP), our aggregates are almost identical to those from SICONFI - for each state, over 75% of deviations are below 1%, and often precisely zero. For PR and RS our deviations are centered around zero but with larger mass slightly above or slightly below - in both states three-quarters of deviations are in the range [-0.5%, 5%], but with more mass for larger absolute deviations in some municipality-years. We also present the same deviations but considering total committed amounts at the municipality-year-function level, where one observation is, for example, the total amount committed by one city for the Health function in 2020. We present these results in Figure A.1, again documenting that our measures of aggregate commitments closely follow those available at SICONFI.

We also provide the distribution of deviations from SICONFI in verification and payment amounts in Figure 4 and Figure 5, respectively. Here our concordance with SICONFI is less precise – while PB and SP show very small deviations, in the remaining states we systematically _overestimate_ total amounts of verification and payments. The distributions of deviations are somewhat similar across states, centered at around 5%-8% and with some mass up to 15%20% in some municipality-years – meaning that the aggregate amount in our data exceeds that in SICONFI by that amount. As reported in Figure A.4 for the states of MG and PR, these deviations do not seem to be driven by specific municipalities or to be systematically different across years, suggesting that there are consistent differences between our measures of verification and payments and those of SICONFI. While we are unable to precisely explain those differences, we expect they are partly driven by amounts carried over across fiscal years, the

> 20We note that SICONFI contains self-reported information and as a result can also contain errors. Moreover, some differences may arise due to different aggregation methods and the inclusion of government entities besides the municipal executive branch, such as state-owned companies.

> 21We use the SICONFI dataset available on the Data Basis ( _Base dos Dados_ ) platform at https://basedosdados.org/dataset/5a3dec52-8740-460e-b31d-0e0347979da0.

> 22We are only able to compute these indicators for municipality-year observations available both in our dataset and SICONFI.

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