and type of address (house, apartment or other) for addresses owned by the survey’s respondents. The estimated coefficients were applied to the same variables of the PNAD-C for the group of homeowners.

**Indirect taxes.** To estimate indirect taxes, we use the estimates from Lara Ibarra et al. (2021) based on the POF survey. To impute the results in PNAD-C, we create a tax burden share (tax burden/consumption) in POF and calculate different measures of the average tax burden shares. These average shares were then smoothed using a _lowess_ estimation. Different options were evaluated: using percentiles of disposable income, percentiles of disposable income by urban and rural, percentiles of tax burden for each group by urban and rural population, and percentiles of tax burden statewide. We opted for using the tax share burden by disposable income per capita percentile in urban and rural zones because it offers a better estimation than the national percentile. The results were comparable to alternative options and, importantly, alternative imputations yield qualitatively similar results. 20F<sup>21</sup> The smoothed tax burden shares were then multiplied by disposable income in PNAD-C. 21F<sup>22</sup>

It should be noted that the range of indirect tax burden is wide. For example, for the tax burden as a ratio to disposable income, the interquartile range is nine, and the difference between the max and minimum is around 39 percentage points. This broad range results from estimating the tax burden from a composite measure of different indirect taxes, with distinct tax bases, rates, and disparate patterns of taxable consumptions according to income. However, the average by decile and urban/rural resulted in a share of outliers (outside the range of the mean +/- two standard deviations) that corresponds to less than 5 percent of the observations.

Table 1 presents the set of taxes and contributions in Brazil in 2019 and indicates whether each of them was included in the analysis. Most policies are explicitly considered in the investigation. Taxes and contributions added up to 44.1% of the Brazilian GDP in 2019. Indirect taxes are the most important component of the GDP share (15.3%), followed by social contributions (11.6%), and direct taxes (9.7%). Indirect taxes represent the highest contribution to government revenues (34.7%). ICMS, in turn, is by far the most relevant tax both in terms of share of GDP or total government revenues, followed by the personal (IRPF) and the corporate income taxes (IRPJ, CSLL and others).

21 The ratio of indirect taxes to MIPP in the urban/rural scenario used in this paper is 16.69 percent, which is close to 16.72 percent in the estimation by state and 16.68 in the nationwide estimation.

> 22 Brazil's indirect tax incidence (i.e. the total indirect tax payments as a share of the total of households’ MIPP) is higher than most other countries in the CEQ sample. Brazil has one of the highest indirect tax incidence (16.7%), trailing only South Africa in 2014/2015 (Goldman et al., 2020), with an estimated incidence of indirect taxes of 19.7%. This figure also exceeds Brazil's estimate for 2009, which was 14.5% (Higgins et al., 2020). Notably, these countries also have a high indirect tax incidence among the poorest households. In 2019, Brazil's first quintile tax incidence was high at 58.1%. This rate is above that of Argentina's 2017 (44%) and Brazil's 2009 estimate (39%), but still below the estimate for South Africa (above 90%). A few factors can explain this high incidence for the first quintile. First, indirect tax collection is high in Brazil, even higher than all tax collection as a share of GDP in other countries. Second, many households in the bottom quintile rely on government transfers and can consume (and pay corresponding consumption taxes) above their market income plus pensions. Third, we use a composite measure that includes different indirect taxes (PIS/COFINS, ISS, and other taxes) to capture the complexity of different levels of government. However, only the POF survey provides all the necessary information for these estimates. The imputation from POF to PNAD-C was based on the smoothed average tax burden of indirect taxes by centile and rural/urban areas, which leaves out some outliers.

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